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Carbon Emission Reduction Effectiveness Dataset in Provinces of China (2005-2016)


CUI Panpan1ZHANG Lijun1QIN Yaochen*1,2
1 College of Geography and Environmental Science / Key Laboratory of Geospatial Technology for the Middle and Lower Yellow River Regions,HenanUniversity,Kaifeng 475004,China2 Key Research Institute of Yellow River Civilization and Sustainable Development & Collaborative Innovation Center on Yellow River Civilization jointlybuilt by Henan Province and Ministry of Education,Henan University,Kaifeng 475004,China

DOI:10.3974/geodb.2022.07.03.V1

Published:July 2022

Visitors:1027       Data Files Downloaded:44      
Data Downloaded:0.81 MB      Citations:

Key Words:

province,energy consumption,carbon emissions intensity,emission reduction effectiveness

Abstract:

The Carbon Emission Reduction Effectiveness Dataset in Provinces of China (2005-2016) was developed based on the energy consumption and economic development data of 30 provinces (data covering Hong Kong, Macao,Taiwan and Tibet were unavailable). The correction coefficient was applied to measure emission reduction efficiency in each province. According to the energy consumption carbon emission intensity and output value share of each province, the equality of national carbon emission intensity in China’s energy consumption was established from top to bottom, and the contribution rate of each province, energy consumption carbon emission intensity and output value share in each province to the decline of national carbon emission intensity in energy consumption was obtained by using LMDI-Ⅰ decomposition method. Following the idea of “Emission reduction efficiency - carbon emission intensity contribution - comprehensive contribution by province - the relationship between provincial emission reduction efficiency and the comprehensive contribution of province”, the emission reduction effectiveness of each province in the process of carbon emission intensity decline in China energy consumption was evaluated. The dataset includes: (1) energy consumption carbon emissions intensity and change in China during 2005-2016; (2) the correction coefficients of provincial energy consumption carbon emission reduction; (3) the decomposition factors’ contribution rate to carbon emission intensity decline in China; (4) the order of provincial emission reduction efficiency and comprehensive contribution. The dataset is archived in .xlsx formats in one file with data size of 19.1 KB. The analysis paper was published on the Geographical Research, Vol.39, No.8, 2020.

Foundation Item:

National Natural Science Foundation of China (42171295, 42071294, 42101206); Henan Province (2019SJGLX043, 222300420030, 222300420132);

Data Citation:

CUI Panpan, ZHANG Lijun, QIN Yaochen*. Carbon Emission Reduction Effectiveness Dataset in Provinces of China (2005-2016)[J/DB/OL]. Digital Journal of Global Change Data Repository, 2022. https://doi.org/10.3974/geodb.2022.07.03.V1.

References:

[1] Wang, W. J., Chen, Z. L. Research on the allocation scheme of initial carbon quota in provincial regions of China: Based on the perspective of responsibility and goal, fairness and efficiency [J]. Management World, 2019, 35(3): 81-98.
     [2] Zhou, P., Wang, M. Carbon dioxide emissions allocation: A review [J]. Ecological Economics, 2016, 125: 47-59.
     [3] Qian, H. Q., Wu, L. B., Ren, F. Z. From “spurring a willing horse” to efficiency driven: A study of China’s regional CO2 emission permit allocation [J]. Economic Research Journal, 2019, (3): 86-102.
     [4] Feng, D., Yu, B. L., Hadachin, T., et al. Drivers of carbon emission intensity change in China [J]. Resources, Conservation & Recycling, 2018, 129: 187-201.
     [5] Junna, Y., Bin, S., Yu, L. Multiplicative structural decomposition and attribution analysis of carbon emission intensity in China, 2002–2012 [J]. Journal of Cleaner Production, 2018, 198: 195-207.
     [6] Chen, C., Zhao, T., Yuan, R., et al. A spatial-temporal decomposition analysis of China’ s carbon intensity from the economic perspective [J]. Journal of Cleaner Production, 2019, 215: 557-569.
     [7] Ye, B., Jiang, J. J., Li, C., et al. Quantification and driving force analysis of provincial-level carbon emissions in China [J]. Applied Energy, 2017, 198: 223-238.
     [8] Lu, W. B., Qiu, T. T., Du, L. A study on influence factors of carbon emissions under different economic growth stages in China [J]. Economic Research Journal, 2013, 48(4): 106-118.
     [9] Sun, Y. Z., Shen, L., Zhong, S., et al. Driving force analysis of carbon emission changes in China [J]. Resources Science, 2017, 39(12): 2265-2274.
     [10] Pan, J. H., Zhang, L. F. Research on the regional variation of carbon productivity in China [J]. China Industrial Economics, 2011(5): 47-57.
     [11] Mielnik, O., Goldemberg, J. The Evolution of the “Carbonization Index” in developing countries [J]. Energy Policy, 1999, 27 (5): 307-308.
     [12] Ma, D. L., Chen, Z. C., Wang, L. Spatial econometrics research on inter-provincial carbon emissions efficiency in China [J]. China Population, Resources and Environment, 2015, 25(1): 67-77.
     [13] Yang, Z., Chen, L. X., Luo T. Marginal cost of emission reduction and regional differences [J]. Journal of Management Sciences in China, 2019, 22(2): 1-21
     [14] Wang, W. W., Li, M., Zhang, M. Study on the changes of the decoupling indicator between energy - related CO2, emission and GDP in China [J]. Energy, 2017, 128: 11-18.
     [15] Huang, R., Liu, C. X. Analysis of the impact of China's participation in global climate governance [J]. Geographical Research, 2017, 36(11): 2213-2224.
     [16] IPCC/OECD. 2006 IPCC Guidelines for National Green-house Gas Inventories [OL]. https://www.ipcc-nggip. iges.or.jp/public/2006gl/pdf/2_Volume2/V2_2_Ch2_Stationary_Combustion.pdf, 2019-08-02.
     [17] Diakoulaki, D., Mandaraka, M. Decomposition analysis for assessing the progress in decoupling industrial growth from CO2 emissions in the EU manufacturing sector [J]. Energy Economics, 2007, 29(4): 636-664.
     [18] Ang, B. W. LMDI decomposition approach: A guide for implementation [J]. Energy Policy, 2015, 86: 233-238.
     

Data Product:

ID Data Name Data Size Operation
1 ProvCReducEffectChina_2005-2016.xlsx 18.87KB
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