Potential assessment of optimizing energy structure in the city of carbon intensity target

Optimizing energy structure to reduce carbon intensity is an effective way to build the low-carbon city development model. To assess the contribution potential of energy structure optimization in order to achieve the carbon intensity target in Hangzhou City, the component data prediction, scenario prediction, GMI (1, 1) model and other multidisciplinary approaches were adopted to predict the carbon intensity trends in the above said City from 2014 to 2020. This contribution potential is evaluated based on 9 kinds of combined scenarios. The results show that: 1. Under the same economic growth rate, with the increase in energy structure adjustment range, the carbon intensity “decline range” becomes larger, and the higher is the “contribution potential” of energy structure optimization to achieve the carbon intensity target. 2. Within the same range of energy structure optimization, the economic growth rate is lower, the carbon intensity “decline range” is smaller, and the “contribution potential” of energy structure optimization is higher for the same carbon intensity objective. By optimizing energy structure and industrial structures adjustment, the technology upgrading for carbon emissions and the scientific and technological level of the systematic industrial policies are more conducive to supporting the realization of low-carbon city.

[1]  Antonio Bonomi,et al.  Economic and environmental assessment of n-butanol production in an integrated first and second generation sugarcane biorefinery: Fermentative versus catalytic routes , 2015 .

[2]  Govinda R. Timilsina.  Economic Implications of Moving Toward Global Convergence on Emission Intensities , 2012 .

[3]  P. Zhou,et al.  On estimating shadow prices of undesirable outputs with efficiency models: A literature review , 2014, Applied Energy.

[4]  Shenggang Ren,et al.  The effects of urbanization, consumption ratio and consumption structure on residential indirect CO2 emissions in China: A regional comparative analysis , 2015 .

[5]  Liang Liang,et al.  A DEA-based approach for fair reduction and reallocation of emission permits , 2013, Math. Comput. Model..

[6]  Alexandre Szklo,et al.  Abatement costs of CO2 emissions in the Brazilian oil refining sector , 2011 .

[7]  Sha Yu,et al.  Role of non-fossil energy in meeting China's energy and climate target for 2020 , 2012 .

[8]  Ming Xu,et al.  China's 2020 carbon intensity target: Consistency, implementations, and policy implications , 2012 .

[9]  Emmanouil Hatzigeorgiou,et al.  CO2 emissions, GDP and energy intensity: A multivariate cointegration and causality analysis for Greece, 1977–2007 , 2011 .

[10]  Li Na,et al.  Intensity Allocation Criteria of Carbon Emissions Permits and Regional Economic Development in China — Based on a 30-Province/Autonomous Region Computable General Equilibrium Model , 2012 .

[11]  Ching-Chih Chang.  A multivariate causality test of carbon dioxide emissions, energy consumption and economic growth in China , 2010 .

[12]  Toshihiko Masui,et al.  Achieving Copenhagen target through carbon emission trading: Economic impacts assessment in Guangdong Province of China , 2015 .

[13]  中華人民共和国国家統計局.  China statistical yearbook , 1988 .

[14]  Yi-Ming Wei,et al.  Energy and emissions efficiency patterns of Chinese regions: A multi-directional efficiency analysis , 2013 .

[15]  Harn Wei Kua,et al.  Lessons for integrated household energy conservation policy from Singapore’s southwest Eco-living Program , 2013 .

[16]  Yongheng Deng,et al.  Economic Returns to Energy-Efficient Investments in the Housing Market: Evidence from Singapore , 2011 .

[17]  Zhou De-qun,et al.  Driving forces of residential CO2 emissions in urban and rural China: An index decomposition analysis , 2010 .

[18]  P. Ekins,et al.  Marginal abatement cost curves: a call for caution , 2011 .

[19]  Limin Du,et al.  Regional allocation of carbon dioxide abatement in China , 2012 .

[20]  Yi-Ming Wei,et al.  Provincial allocation of carbon emission reduction targets in China: An approach based on improved fuzzy cluster and Shapley value decomposition , 2014 .

[21]  James B. Ang,et al.  CO2 Emissions, Research and Technology Transfer in China , 2009 .

[22]  Tek Narayan Maraseni,et al.  Selecting a CDM investor in China: A critical analysis , 2013 .

[23]  Ekundayo Shittu,et al.  Technical change and the marginal cost of abatement , 2008 .

[24]  Martin Skitmore,et al.  Review of social water cycle research in a changing environment , 2016 .

[25]  Jun Bi,et al.  Chinese regional industrial energy efficiency evaluation based on a DEA model of fixing non-energy inputs , 2010 .

[26]  Dequn Zhou,et al.  Modeling economic performance of interprovincial CO2 emission reduction quota trading in China , 2013 .

[27]  Sonja Peterson,et al.  Marginal Abatement Cost Curves in General Equilibrium: The Influence of World Energy Prices , 2004 .

[28]  Ernst Worrell,et al.  Energy efficiency in the German pulp and paper industry – A model-based assessment of saving potentials , 2012 .

[29]  Mikiko Kainuma,et al.  Low-carbon transitions in world regions: comparison of technological mitigation potential and costs in 2020 and 2030 through bottom-up analyses , 2012, Sustainability Science.

[30]  Elif Akbostancı,et al.  CO2 emissions of Turkish manufacturing industry: A decomposition analysis , 2011 .

[31]  P. Zhou,et al.  Efficiency and abatement costs of energy-related CO2 emissions in China: A slacks-based efficiency measure , 2012 .

[32]  Yi-Ming Wei,et al.  China’s regional industrial energy efficiency and carbon emissions abatement costs , 2014 .

[33]  Edgar G. Hertwich,et al.  Pollution embodied in trade: The Norwegian case , 2006 .

[34]  Sergey Paltsev,et al.  Marginal Abatement Costs and Marginal Welfare Costs for Greenhouse Gas Emissions Reductions: Results from the EPPA Model , 2011, Environmental Modeling & Assessment.

[35]  Andrés J. Picazo-Tadeo,et al.  Are the determinants of CO2 emissions converging among OECD countries , 2013 .

[36]  Megan Mehaffey,et al.  Using Malmquist Indices to evaluate environmental impacts of alternative land development scenarios , 2013 .

[37]  J. Kaivo-oja,et al.  Trend analysis of energy and climate policy environment: Comparative electricity production and consumption benchmark analyses of China, Euro area, European Union, and United States , 2016 .

[38]  Zhang Yi,et al.  The Cost Saving Effect of Carbon Markets in China for Achieving the Reduction Targets in the "12th Five-Year Plan" , 2013 .