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Technological Abrupt Change in the Process of Low Carbon Transformation in High-carbon Industry considering Energy Rebound Effect |
Zhang Jijian,Wan An'wei,Song Yajing |
(School of Finance and Economics,Jiangsu University,Zhenjiang 212013,China) |
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Abstract Specifically,considering the rebound effect of energy,the technological threshold of low-carbon technology mutations in the high-carbon industry is analyzed.On the one hand,it indicates that the threshold effect of technological carbon emission reduction has changed from qualitative to qualitative change.Effect.Using the energy rebound model of the Hansen threshold panel model combined with the CD production function,it is found that:①if the energy rebound effect is not considered,then there are two thresholds for technological mutations in the low-carbon process of the high-carbon industry;②if energy is considered The rebound effect will be reduced to a threshold,and the time required for the high-carbon industry to complete the mutation of low-carbon technology will be extended.Therefore,in order to accelerate the mutation of low-carbon technologies in the high-carbon industry and effectively reduce carbon emissions,on the one hand,the impact of the energy rebound effect must be alleviated.For high-carbon industries with large energy rebound effects,real-time monitoring is adopted,and the energy rebound is high The carbon industry adopts regular inspection and irregular sampling inspection to monitor; monetary policy,fiscal policy and industrial policy cooperate with each other to maximize the control of the energy rebound effect while the high-carbon industry drives economic growth.On the other hand,the market approach and government functions go hand in hand,assisting enterprises with low-carbon technology upgrades and equipment transformation,optimizing tax structures,making up for the negative externalities caused by low-carbon technology upgrades,and improving high-carbon industry transfer or exit market mechanisms.
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Received: 30 July 2020
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[1] 张恪渝,廖明球,杨军.绿色低碳背景下中国产业结构调整分析[J].中国人口·资源与环境,2017,27(3): 116-122.[2] 李平.社会-技术范式视角下的低碳转型[J].科学学研究,2018,36(6):1000-1007.[3] 张盼,熊中楷.基于政府视角的最优碳减排政策研究[J].系统工程学报,2018,33(5): 627-636.[4] 邵帅,张可,豆建民.经济集聚的节能减排效应: 理论与中国经验[J].管理世界,2019 (1): 36-59.[5] 赵黎明,殷建立.碳交易和碳税情景下碳减排二层规划决策模型研究[J].管理科学,2016(1): 137-146.[6] 程时雄,柳剑平,龚兆鋆.中国工业行业节能减排经济增长效应的测度及影响因素分析[J].世界经济,2016,39(3): 166-192.[7] 周喜君.中国二氧化碳减排过程中的技术偏向研究[J].科研管理,2018,39(5):29-37.[8] 黄凌云,谢会强,刘冬冬.技术进步路径选择与中国制造业出口隐含碳排放强度[J].中国人口·资源与环境,2017,27(10):94-102.[9] 刘朝,周宵宵,张欢,等.中国居民能源消费间接回弹效应测算:基于投入产出和再分配模型的研究[J].中国软科学,2018,334(10):147-162.[10] BENTZEN J.Estimating the rebound effect in US manufacturing energy consumption[J].Energy Economics,2004,26(1):123-134.[11] LIN B,LIU X.Dilemma between economic development and energy conservation:energy rebound effect in China[J].Energy,2012,45(1):867-873.[12] LIN B,LI J.The rebound effect for heavy industry:empirical evidence from China[J].Energy Policy,2014,74:589-599.[13] LIN B,TIAN P.The energy rebound effect in China's light industry: atranslog cost function approach[J].Journal of Cleaner Production,2016,112:2793-2801.[14] LIN B,ZHAO H.Technological progress and energy rebound effect in China?s textile industry:evidence and policy implications[J].Renewable and Sustainable Energy Reviews,2016,60:173-181.[15] LIN B,XIE X.Factor substitution and rebound effect in China's food industry[J].Energy Conversion and Management,2015,105:20-29.[16] 李小平,卢现祥.国际贸易、污染产业转移和中国工业CO2排放[J].经济研究,2010(1):15-26.[17] 王明喜,胡毅,郭冬梅,等.低碳经济:理论实证研究进展与展望[J].系统工程理论与实践,2017,37(1):17-34.[18] 马丽梅,史丹,裴庆冰.中国能源低碳转型(2015-2050):可再生能源发展与可行路径[J].中国人口·资源与环境,2018,28(2):8-18.[19] OHNISHI S,FUJII M,OHATA M,et al.Efficient energy recovery through a combination of waste-to-energy systems for a low-carbon city[J].Resources,Conservation and Recycling,2018,128: 394-405.[20] HAYASHI D.Knowledge flow in low-carbon technology transfer:a case of India's wind power industry[J].Energy Policy,2018,123: 104-116.[21] 崔强,徐鑫,匡海波.基于RM-DEMATEL的交通运输低碳化能力影响因素分析[J].管理评论,2018(1):210-220.[22] SETO K C,DAVIS S J,MITCHELL R B,et al.Carbon lock-in: types,causes,and policy implications[J].Annual Review of Environment and Resources,2016,41: 425-452.[23] MATTAUCH L,CREUTZIG F,EDENHOFER O.Avoiding carbon lock-in: policy options for advancing structural change[J].Economic Modelling,2015,50: 49-63.[24] 吴力波,钱浩祺,汤维祺.基于动态边际减排成本模拟的碳排放权交易与碳税选择机制[J].经济研究,2014(9):48-61.[25] 张晓娣,刘学悦.征收碳税和发展可再生能源研究——基于OLG-CGE模型的增长及福利效应分析[J].中国工业经济,2015(3):18-30.[26] TRICCO A C,LILLIE E,ZARIN W,et al.A scoping review on the conduct and reporting of scoping reviews [J].Bmc Medical Research Methodology,2016,16(1):1-10.[27] HAMILTON J,MAYNE R,PARAG Y,et al.Scaling up local carbon action: the role of partnerships,networks and policy[J].Carbon Management,2014,5(4):463-476.[28] HAELG L,WAELCHLI M,SCHMIDT T S.Supporting energy technology deployment while avoiding unintended technological lock-in: a policy design perspective[J].Environmental Research Letters,2018,13(10): 104011.[29] 朱瑞博.核心技术链、核心产业链及其区域产业跃迁式升级路径[J].经济管理,2011(4):43-53.[30] ZHU S,YE A.Does the impact of China's outward foreign direct investment on reverse green technology process differ across countries[J].Sustainability,2018,10(11): 3841.[31] WONG C Y,GOH K L.Catch-up models of science and technology: a theorization of the Asian experience from bi-logistic growth trajectories[J].Technological Forecasting and Social Change,2015,95: 312-327.[32] FURUKAWA Y.Leapfrogging cycles in international competition[J].Economic Theory,2015,59(2): 401-433.[33] WANG N,WEI W.China's regional rebound effect based on modelling multi-regional CGE[J].Applied Economics,2019: 1-15.[34] LIN B,TAN R.Estimating energy conservation potential in China's energy intensive industries with rebound effect[J].Journal of Cleaner Production,2017,156: 899-910.[35] LU M,WANG Z.Rebound effects for residential electricity use in urban China: an aggregation analysis based EIO and scenario simulation [J].Annals of Operations Research,2017,255(1-2): 525-546.[36] HAN H,HAWKEN S.Introduction:innovation and identity in next-generation smart cities[J].City,Culture and society,2018,12: 1-4.[37] 冯烽.能效改善与能源节约: 助力还是阻力——基于中国 20 个行业能源回弹效应的分析[J].数量经济技术经济研究,2018 (2): 82-98.[38] KARMELLOS M,KOPIDOU D,DIAKOULAKI D.A decomposition analysis of the driving factors of CO2 (Carbon dioxide) emissions from the power sector in the European Union countries[J].Energy,2016,94: 680-692.[39] 李小胜,安庆贤.环境管制成本与环境全要素生产率研究[J].世界经济,2012(12):23-40.[40] 马大来,陈仲常,王玲.中国省际碳排放效率的空间计量[J].中国人口·资源与环境,2015,173(1):67-77.[41] 沈能,王艳,王群伟.集聚外部性与碳生产率空间趋同研究[J].中国人口·资源与环境,2013,23(12):40-47.[42] 李筱乐.市场化、工业集聚和环境污染的实证分析[J].统计研究,2014,31(8):39-45.[43] SU H N,MOANIBA I M.Does innovation respond to climate change? empirical evidence from patents and greenhouse gas emissions[J].Technological Forecasting and Social change,2017,122(9):49-62.[44] 查建平,唐方方,别念民.结构性调整能否改善碳排放绩效——来自中国省级面板数据的证据[J].数量经济技术经济研究,2012(11):18-33.[45] 王为东,卢娜,张财经.空间溢出效应视角下低碳技术创新对气候变化的响应[J].中国人口·资源与环境,2018,28 (8):25-33.[46] 韩坚,盛培宏.产业结构、技术创新与碳排放实证研究——基于我国东部15个省(市)面板数据[J].上海经济研究,2014(8):67-74.[47] 原嫄,席强敏,孙铁山,等.产业结构对区域碳排放的影响——基于多国数据的实证分析[J].地理研究,2016,35(1):82-94.[48] 徐成龙,任建兰,巩灿娟.产业结构调整对山东省碳排放的影响[J].自然资源学报,2014,29(2):201-210.[49] 谢静,马爱霞.创新视角下我国医药制造业集聚水平分析——基于DO指数的企业精准地理位置测度[J].科技管理研究,2017,37(15):178.[50] 魏峰, 武晓明.基于投入产出表的中国房地产业经济效应分析[J].统计与决策,2017(18):146-149. |
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