References
- [1] Zhang H, Chen J, Li Y, Seiler MJ. Does the Development of China’s Building Industry Influence the Global Energy Consumption and Carbon Emissions? an Analysis Based on the GVAR Model. Singapore: Springer; 2018. DOI: 10.1007/978-981-10-6190-5_58.10.1007/978-981-10-6190-5_58
- [2] Governments, USaC. U.S.-China Joint Announcement on Climate Change. 2014. Available from: https://obamawhitehouse.archives.gov/the-press-office/2014/11/11/us-china-joint-announcement-climate-change.
- [3] Jiang J, Ye B, Liu J. Peak of CO2 emissions in various sectors and provinces of China: Recent progress and avenues for further research. Renew Sust Energy Rev. 2019;112:813-33. DOI: 10.1016/j.rser.2019.06.024.10.1016/j.rser.2019.06.024
- [4] IPCC (2008). 2006 IPCC Guidelines for National Greenhouse Gas Inventories. Available from: https://www.ipcc.ch/report/2006-ipcc-guidelines-for-national-greenhouse-gas-inventories/.
- [5] IPCC (2019). 2019 Refinement to the 2006 IPCC Guidelines for National Greenhouse Gas Inventories. Available from: https://www.ipcc.ch/report/2019-refinement-to-the-2006-ipcc-guidelines-for-national-greenhouse-gas-inventories/.
- [6] Lin BQ, Xu B. Growth of industrial CO2 emissions in Shanghai city: Evidence from a dynamic vector autoregression analysis. Energy Oxford. 2018;151:167-77. DOI: 10.1016/j.energy.2018.03.052.10.1016/j.energy.2018.03.052
- [7] Wen L, Zhang X. CO2 emissions in China’s Yangtze River Economic Zone: A dynamic vector autoregression approach. Pol J Environ Stud. 2019;28:923-33. DOI: 10.15244/pjoes/83668.10.15244/pjoes/83668
- [8] Xu B, Lin BQ. What cause a surge in China’s CO2 emissions? A dynamic vector autoregression analysis. J Clean Prod. 2016;143:17-26. DOI: 10.1016/j.jclepro.2016.12.159.10.1016/j.jclepro.2016.12.159
- [9] Xu B, Lin BQ. Assessing CO2 emissions in China’s iron and steel industry: A dynamic vector autoregression model. Appl Energy. 2016;161:357-86. DOI: 10.1016/j.apenergy.2015.10.039.10.1016/j.apenergy.2015.10.039
- [10] Hao H, Geng Y, Li W, Guo B. Energy consumption and GHG emissions from China’s freight transport sector: Scenarios through 2050. Energy Policy. 2015;85:94-101. DOI: 10.1016/j.enpol.2015.05.016.10.1016/j.enpol.2015.05.016
- [11] Shao S, Liu J, Geng Y, Miao Z, Yang Y. Uncovering driving factors of carbon emissions from China’s mining sector. Appl Energy. 2016;166:220-38. DOI: 10.1016/j.apenergy.2016.01.047.10.1016/j.apenergy.2016.01.047
- [12] Huang WL, Yin X, Chen WY. Prospective scenarios of CCS implementation in China’s power sector: An analysis with China TIMES. Energy Procedia. 2014;61:937-40. DOI: 10.1016/j.egypro.2014.11.999.10.1016/j.egypro.2014.11.999
- [13] Lin B, Moubarak M, Ouyang XL. Carbon dioxide emissions and growth of the manufacturing sector: Evidence for China. Energy. 2014;76:830-7. DOI: 10.1016/j.energy.2014.08.082.10.1016/j.energy.2014.08.082
- [14] Shi Q, Chen J, Shen L. Driving factors of the changes in the carbon emissions in the Chinese construction industry. J Clean Prod. 2017;(166):615-27. DOI: 10.1016/j.jclepro.2017.08.056.10.1016/j.jclepro.2017.08.056
- [15] Yang T, Pan Y, Yang Y, Lin M, Qin B, Xu P, et al. CO2 emissions in China’s building sector through 2050: A scenario analysis based on a bottom-up model. Energy. 2017;128:208-23. DOI: 10.1016/j.energy.2017.03.098.10.1016/j.energy.2017.03.098
- [16] Ai F, Yin X, Hu R, Ma H, Liu W. Research into the super-absorbent polymers on agricultural water. Agr Water Manage. 2021:106513. DOI: 10.1016/j.agwat.2020.106513.10.1016/j.agwat.2020.106513
- [17] Zhang X, Zang C, Ma H, Wang Z. Study on removing calcium carbonate plug from near wellbore by high-power ultrasonic treatment. Ultrason Sonochem. 2020:104515. DOI: 10.1016/j.ultsonch.2019.03.006.10.1016/j.ultsonch.2019.03.00631753547
- [18] Mo L, Sun W, Jiang S, Zhao X, Ma H, Liu B, et al. Removal of colloidal precipitation plugging with high-power ultrasound. Ultrason Sonochem. 2020;69:105259. DOI: 10.1016/j.ultsonch.2020.105259.10.1016/j.ultsonch.2020.10525932738455
- [19] Liu W, Ma H, Walsh A. Advance in photonic crystal solar cells. Renew Sust Energy Rev. 2019;116:109436. DOI: 10.1016/j.rser.2019.109436.10.1016/j.rser.2019.109436
- [20] Ma H, Zhang X, Ju F, Tsai SB. A study on curing kinetics of nano-phase modified epoxy resin. Sci Rep. 2018;8. DOI: 10.1038/s41598-018-21208-0.10.1038/s41598-018-21208-0581301729445228
- [21] Ma H, Tsai SB. Design of research on performance of a new iridium coordination compound for the detection of Hg2+. Int J Env Res Pub HE. 2017;14. DOI: 10.3390/ijerph14101232.10.3390/ijerph14101232566473329035349
- [22] Yang G, He XL, Li JF, Jia XJ. The research of water resource sustainable utilization in Manas River. Acta Ecologica Sinica. Available from: https://www.oalib.com/paper/1402574.
- [23] Gendron C. Beyond environmental and ecological economics: Proposal for an economic sociology of the environment. Ecol Econ. 2014;105:240-53. DOI: 10.1016/j.ecolecon.2014.06.012.10.1016/j.ecolecon.2014.06.012
- [24] Men B, Liu H, Tian W, Liu H. Evaluation of sustainable use of water resources in Beijing based on rough set and fuzzy theory. Water. 2017;9:852. DOI: 10.3390/w10070925.10.3390/w10070925
- [25] Wu X, Wen QB, Hu LM, Liu MY. Evaluation of unconventional water resources based on knowledge granularity. E3S Web Conf. 2020;144(1-3):01004. DOI: 10.1051/e3sconf/202014401004.10.1051/e3sconf/202014401004
- [26] Pawlak Z. Rough sets. Int J Comput Inform Sci. 1982;11:341-56. DOI: 10.1007/BF01001956.10.1007/BF01001956
- [27] Pawlak Z. Rough classification. Int J Man Mach Stud. 1984;20:469-83. DOI: 10.1016/S0020-7373(84)80022-X.10.1016/S0020-7373(84)80022-X
- [28] Pawlak Z. Rough sets and intelligent data analysis. Inform Sci. 2002;147:1-12. DOI: 10.1016/S0020-0255(02)00197-4.10.1016/S0020-0255(02)00197-4
- [29] Pawlak Z, Skowron A. Rough sets: Some extensions. Inform Sci. 2007;177:28-40. DOI: 10.1016/j.ins.2006.06.006.10.1016/j.ins.2006.06.006