能源消费碳排放是造成全球气候变暖的主要原因，为减少温室气体排放量，世界各国均承担起相应的减排责任。据统计数据分析，目前我国的碳排放总量已居世界首位，解决碳排放问题刻不容缓，为此我国在第75届联合国大会上提出了“双碳”目标。省域经济发展水平、资源禀赋等有所差异，导致能源的消费量与消费结构存在差异，把握能源消费碳排放的时空演进趋势并甄别其驱动因素对实现“双碳”目标至关重要。

基于2003-2019年我国相关统计数据，从全国、区域及省域层面分析能源消费碳排放的时空演进特征；以STIRPAT模型为基础，分析人口、财富、技术及自然环境对碳排放的影响，其次考虑政府调控、市场调节及生产消费者的应对策略从多维视角构建碳排放驱动机制模型，借鉴面板数据分析、中介及调节效应检验法，分别从全国及省域视角甄别诸因素对碳排放的作用路径。

研究结果表明：碳排放演进方面，我国碳排放处于倒“U”型曲线左侧的增长阶段，涨幅趋于平缓；总体上呈北多南少，西多东少态势，且具有显著的空间聚集性特征。碳排放驱动机制方面，全国层面，从碳排放的直接驱动因素看，环境规制与碳排放间存在倒“U”型关系，经济增长、城镇化、以煤炭占比表达的能源结构、温度、国际贸易和国内贸易均正向驱动碳排放，以第三产业占比表达的产业结构、以65岁以上人口占比表达的人口结构及能源价格均负向驱动碳排放。从碳排放的间接驱动因素看，技术创新在环境规制与碳排放间起负向中介作用，能源价格在二者间起正向调节作用，而国内贸易、国际贸易在二者间起负向调节作用。省域层面，从碳排放的直接驱动因素看，环境规制与碳排放间存在“U”型、倒“U”型及直线型三种关系，经济增长正向驱动各省碳排放，能源价格负向驱动各省碳排放，其他因素对碳排放的驱动作用表现出省域差异性。从碳排放的间接驱动因素看，技术创新的负向中介作用仅在山西、内蒙古、辽宁、黑龙江、山东、河南、四川、贵州、陕西、甘肃、新疆省份显著，能源价格正向调节环境规制的减排作用，国内、国际贸易负向调节环境规制的减排作用。

Carbon emissions from energy consumption is the main causes of global warming. In order to reduce greenhouse gas emissions, all countries in the world bear the corresponding responsibility for emission reduction. According to statistical data analysis, China's total carbon emissions have ranked first in the world at present, so it is urgent to solve the problem of carbon emissions. Therefore, China proposed the “double carbon” goal at the 75th United Nations General Assembly. Differences in provincial economic development level and resource endowment lead to differences in energy consumption and consumption structure. It is essential to grasp the spatio-temporal evolution trend of carbon emissions of energy consumption and identify its driving factors to achieve the goal of “double carbon”.

Based on the relevant statistical data of China from 2003 to 2019, this paper analyzes the spatial and temporal evolution characteristics of carbon emissions from energy consumption at the national, regional and provincial levels. Based on the STIRPAT model, this paper analyzes the impact of population, wealth, technology and natural environment on carbon emissions. Secondly, considering the government regulation, market regulation and the coping strategies of production consumers, the carbon emission driving mechanism model is constructed from a multidimensional perspective. Using panel data analysis, intermediary and regulatory effect test methods, the path of various factors on carbon emissions is screened from the national and provincial perspectives.

The results show that: in terms of carbon emissions evolution, China's carbon emissions are in the growth stage on the left side of the inverted “U” curve, and the increase tends to be gentle; generally more in the north and less in the south, and more in the west and less in the east, and have obvious spatial agglomeration characteristics. At the national level, from the direct driving factors of carbon emissions, there is an inverted “U” type relationship between environmental regulation and carbon emissions. Economic growth, urbanization, energy structure expressed by coal proportion, temperature, international trade and domestic trade are positively driving carbon emissions. Industrial structure expressed by the proportion of tertiary industry, population structure expressed by the proportion of people over 65 years old and energy prices are negatively driving carbon emissions. From the perspective of indirect driving factors of carbon emissions, technological innovation plays a negative mediating role between environmental regulation and carbon emissions, energy price plays a positive moderating role between them, and domestic trade and international trade play a negative moderating role between them. At the provincial level, from the perspective of the direct driving factors of carbon emissions, there are three types of relationships between environmental regulation and carbon emissions: “U”, inverted “U” and linear. Economic growth positively drives carbon emissions in each province, while energy price negatively drives carbon emissions in each province. The driving effects of other factors on carbon emissions show provincial differences. From the perspective of indirect driving factors of carbon emissions, the negative mediating effect of technological innovation is only obvious in Shanxi, Inner Mongolia, Liaoning, Heilongjiang, Shandong, Henan, Sichuan, Guizhou, Shaanxi, Gansu and Xinjiang provinces. Energy prices positively regulate the emission reduction effect of environmental regulation, and domestic and international trade negatively regulate the emission reduction effect of environmental regulation.

[1]吕洁华,张泽野.中国省域碳排放核算准则与实证检验[J].统计与决策,2020,36(03):46-51.

[2]朱妍.治污见成效攻坚进行时[N].中国能源报,2019-12-30(010).

[3]Wu W Q, Ma X, Zhang Y Y, et al. A novel conformable fractional non-homogeneous grey model for forecasting carbon dioxide emissions of BRICS countries[J]. Science of the Total Environment,2020, 707:135447.

[4]Nassar R, Napier‐Linton L, Kevin R. et al. Improving the temporal and spatial distribution of CO2 emissions from global fossil fuel emission data sets[J]. Journal of Geophysical Research: Atmospheres, 2013,118(2),917-913.

[5]Akimoto H, Narita H. Distribution of SO2, NOx and CO2 eissions from fuel combustion and industrial activities in Asia with resolution[J]. Atmospheric Environment,1994,28(2),213-225.

[6]Gurney K R, Igor R, Yang S, et al. Quantification of fossil fuel CO2 emissions on the building/street scale for a large U.S. city[J]. Environmental Science & Technology,2012,46(21),12194-12202.

[7]Kolstad, Charles. Intermediate environmental economics: International edition[M]. Oxford University Press: New York, 2011.

[8]Khurshid A, Deng X. Innovation for carbon mitigation: a hoax or road toward green growth? Evidence from newly industrialized economies[J]. Environmental Science and Pollution Research International, 2020,28(6),6392-6404.

[9]Zhang M, Mu H L, Ning Y D. Accounting for energy-related CO2 emission in China, 1991-2006[J]. Energy Policy,2009,37(3),767-773.

[10]Karmellos M, Kosmadakis V, Dimas P, et al. A decomposition and decoupling analysis of carbon dioxide emissions from electricity generation: Evidence from the EU-27 and the UK[J]. Energy,2021, 231:120861.

[11]Nima R K, Saeed S. The driving forces of energy consumption and carbon dioxide emissions in Iran's electricity sector: A decomposition analysis based on types of ownership[J]. Cleaner Environmental Systems,2021,2:100012.

[12]Abdulrasheed Z, Jurij T, Missaoui I, et al. Impact of Nigeria's industrial sector on level of inefficiency for energy consumption: Fisher Ideal index decomposition analysis[J]. Heliyon,2021,7(5): e06952.

[13]Charfeddine L, Kahia M. Impact of renewable energy consumption and financial development on CO2 emissions and economic growth in the MENA region: A panel vector autoregressive (PVAR) analysis[J]. Renewable Energy,2019,139:198-213.

[14]Mahmood A, Yousaf R M. Role of public-private partnerships investment in energy and technological innovations in driving climate change: evidence from Brazil[J]. Environmental Science and Pollution Research International,2020,27(24),30638-30648.

[15]Simplice A A, Oluwatoyin M, Agboola, Alola A A, et al. The criticality of growth, urbanization, electricity and fossil fuel consumption to environment sustainability in Africa[J]. Science of the Total Environment,2020,712:136376.

[16]Akadiri S S, Bekun F V, Sarkodie S A. Contemporaneous interaction between energy consumption, economic growth and environmental sustainability in South Africa: What drives what? [J]. Science of the Total Environment,2019,686:468-475.

[17]Adedoyin F F, Gumede M I, Bekun F V, et al. Modelling coal rent, economic growth and CO2 emissions: Does regulatory quality matter in BRICS econ-omies? [J]. Science of the Total Environment,2020, 710:136284.

[18]Ullah K Z, Manzoor A, Anwar K. On the remittances-environment led hypothesis: Empirical evidence from BRICS economies[J]. Environmental Science and Polluteon Research International,2020,27(14): 16460-16471.

[19]Takayabu H, Kagawa S, Fujii H, et al. Impacts of productive efficiency improvement in the global metal industry on CO2 emissions[J]. Journal of Environmental Management,2019,248:109261.

[20]Kim T J, Nikolas T. Analysis of carbon emissions embodied in South Korea's international trade: Production-based and consumption-based perspectives[J]. Journal of Cleaner Production,2021,320: 128839.

[21]Danish, Baloch M A, Mahmood N, et al. Effect of natural resources, renewable energy and economic development on CO2 emissions in BRICS countries[J]. Science of the Total Environment,2019,678:632-638.

[22]Wang Q, Jiang X T, Yang X, et al. Comparative analysis of drivers of energy consumption in China, the USA and India - A perspective from stratified heterogeny-eity[J]. The Science of the Total Environment, 2020,698:134117.

[23]尹龙,杨亚男,章刘成.中国居民消费碳排放峰值预测与分析[J].新疆社会科学,2021(04):42-50+168.

[24]方琦,钱立华,鲁政委.我国实现碳达峰与碳中和的碳排放量测算[J].环境保护,2021,49(16):49-54.

[25]邓光耀,任苏灵.中国能源消费碳排放的动态演进及驱动因素分析[J].统计与决策,2017(18):141-143.

[26]潘小海,梁双,张茗洋.我国碳排放分布特征与动态演进研究[J].中国工程咨询,2021(09):27-34.

[27]黄和平,李亚丽,杨斯玲.中国城镇居民食物消费碳排放的时空演变特征分析[J].中国环境管理,2021,13(01):112-120.

[28]胡渊,刘峻峰,胡伟,张震.中国碳排放强度的区域差异、趋势演进与影响因素分析——基于30个省(市、区)1997—2012年面板数据[J].资源与产业,2016,18(05):7-13.

[29]赵桂梅,陈丽珍,孙立成,赵桂芹.空间分异视角下中国碳排放强度的Markov稳态预测[J].科技管理研究,2017,37(22):228-233.

[30]郭文,孙涛.人口结构变动对中国能源消费碳排放的影响——基于城镇化和居民消费视角[J].数理统计与管理,2017,36(02):295-312.

[31]马光辉,张迎春,姚芳斌.中国渔业经济碳排放驱动因素研究——基于LMDI和脱钩模型的双视角分析[J].青岛大学学报(自然科学版),2022,35(01):117-123.

[32]马颖,邵长秀.基于LMDI的北上津区域能源消费碳排放影响因素分析及脱钩效应研究[J].甘肃科学学报,2022,34(01):124-132.

[33]冯宗宪,高赢.中国区域碳排放驱动因素、减排贡献及潜力探究[J].北京理工大学学报(社会科学版),2019,21(04):13-20.

[34]唐华一.技术创新对我国低碳经济发展的影响机制研究[D].南京邮电大学,2016.

[35]戚晓红,公维凤,王传会.城镇化水平与能源消费碳排放的关系探讨——以山东省为例[J].北方经济,2021(03):55-58.

[36]张翠菊,覃明锋.基于时间序列数据的中国碳排放强度影响因素协整分析[J].生态经济,2017,33(03):53-56.

[37]杨青林,赵荣钦,丁明磊,满洲,王帅,余娇,杨文娟.中国城市碳排放的空间格局及影响机制——基于285个地级市截面数据的分析[J].资源开发与市场,2018,34(09):1243-1249.

[38]吴玉鸣.中国省域旅游业弹性系数的空间异质性估计——基于地理加权回归模型的实证[J].旅游学刊,2013,28(02):35-43.

[39]张腾飞,杨俊,盛鹏飞.城镇化对中国碳排放的影响及作用渠道[J].中国人口•资源与环境,2016,26(02):47-57.

[40]韩金雨,曲建升,徐丽,李恒吉,刘莉娜.支农财政对农业碳排放强度的空间效应：基于空间杜宾模型的实证分析[J].生态与农村环境学报,2021,37(11):1404-1412.

[41]刘贤赵,高长春,张勇,余光辉,宋炎.中国省域能源消费碳排放空间依赖及影响因素的空间回归分析[J].干旱区资源与环境,2016,30(10):1-6.

[42]杨建平,杨娟.低碳试点政策的碳减排与空间溢出效应研究[J/OL].经营与管理:2020,1-14.

[43]董梅,李存芳.低碳省区试点政策的净碳减排效应[J].中国人口•资源与环境,2020,30(11):63-74.

[44]戴嵘,曹建华.中国首次“低碳试点”政策的减碳效果评价——基于五省八市的DID估计[J].科技管理研究,2015,35(12):56-61.

[45]武良鹏,陈晔,徐海燕.效果—成本视角下中国各省份碳排放减排路径对比研究[J].软科学,2018,32(12):1-6.

[46]佟昕,陈凯,李刚.中国碳排放与影响因素的实证研究——基于2000～2011年中国以及30个省域的灰色关联分析[J].工业技术经济,2015,34(03):66-78.

[47]郭朝先.中国工业碳减排潜力估算[J].中国人口•资源与环境,2014,24(09):13-20.

[48]李华,马进.环境规制对碳排放影响的实证研究——基于扩展STIRPAT模型[J].工业技术经济,2018,37(10):143-149.

[49]王韧.环境规制与绿色技术创新的动态关联——基于“波特假说”的再检验[J].科技管理研究,2020,40(08):243-250.

[50]徐盈之,杨英超,郭进.环境规制对碳减排的作用路径及效应——基于中国省级数据的实证分析[J].科学学与科学技术管理,2015,36(10):135-146.

[51]王康,李志学,周嘉.环境规制对碳排放时空格局演变的作用路径研究——基于东北三省地级市实证分析[J].自然资源学报,2020,35(02):343-357.

[52]曲健莹,李科.工业增长与二氧化碳排放“脱钩”的测算与分析[J].西安交通大学学报(社会科学版),2019,39(05):92-104.

[53]陈素琴,王晨.基于低碳经济的企业绩效评价指标体系研究[J].财会通讯,2013(21):101-103.

[54]李超逸.低碳经济的理论基础及其经济学价值研究[J].新经济,2014(11):20-21.

[55]方大春,马为彪.中国区域创新与产业结构耦合协调度及其经济效应研究[J].当代经济管理,2019,41(07):50-58.

[56]刘建文.低碳经济的发展模式研究[J].环渤海经济瞭望,2019(07):12-13.

[57]赵海蕊.中国省域碳排放效率演变特征测度及影响因素研究[D].华北电力大学,2019.

[58]Turner R K, Pearce D W, et al. Environmental economics: an elementary introduction[M]. Johns Hopkins University Press, 1993.

[59]杨国武.可持续发展的基本内涵和立法进程[J].中华辞赋,2018(07):15+17.

[60]张云波.中国省域碳排放强度的空间效应研究[D].山西财经大学,2021.

[61]孙琳惠.人口学因素对OECD国家绿色发展的影响机制研究[D].山东师范大学,2019.

[62]吴昊,车国庆.中国人口年龄结构如何影响了地区碳排放?——基于动态空间STIRPAT模型的分析[J].吉林大学社会科学学报,2018,58(03):67-77+204-205.

[63]张忠杰,李真真,李宪慧.金融发展、城镇化对人均能源消费碳排放的影响[J].统计与决策,2020,36(08):106-110.

[64]袁博.城镇化对金砖国家碳排放影响的面板分位计量研究[D].东北财经大学,2019.

[65]Panayotou, T. Empirical tests and policy analysis of environmental degradation at different stages of economic developmen[R]. International Labour Organization,1993.

[66]刘降斌,刘秋明.空间视角下经济增长、城镇化与二氧化碳排放强度关系研究[J].商业经济研究,2021(12):184-188.

[67]张志新,黄海蓉,林立.贸易开放、经济增长与碳排放关系分析——基于“一带一路”沿线国家的实证研究[J].软科学,2021,35(10):44-48.

[68]李慧敏,杨旭,吴相利,贾艳红.时空视角下能源密集型产业结构演变的碳排放效应[J].环境科学学报,2021,41(05):2018-2028.

[69]张志远.碳排放、产业结构与经济增长[D].河南大学,2020.

[70]单欣.基于LMDI法与STIRPAT模型的中国碳排放量影响因素研究[J].节能,2021,40(05):54-57.

[71]Sinn H W. Public policies against global warming: a supply side approach[J]. International Tax and Public Finance,2008,15(4),360-394..

[72]克劳迪娅•凯姆弗特. “绿色悖论”子虚乌有[N].中国能源报, 2009-08-10,(B04). [73]张华,魏晓平.绿色悖论抑或倒逼减排——环境规制对碳排放影响的双重效应[J].中国人口•资源与环境,2014,24(09):21-29.

[74]董棒棒,李莉,唐洪松,苏洋.环境规制、FDI与能源消费碳排放峰值预测——以西北五省为例[J].干旱区地理,2019,42(03):689-697.

[75]杨浩.中国技术创新对二氧化碳排放影响的系统动力学研究[D].重庆工商大学,2020.

[76]卢娜,王为东,王淼,张财经,陆华良.突破性低碳技术创新与碳排放:直接影响与空间溢出[J].中国人口•资源与环境,2019,29(05):30-39.

[77]王鑫静,程钰,丁立,王建事.“一带一路”沿线国家科技创新对碳排放效率的影响机制研究[J].软科学,2019,33(06):72-78.

[78]陈冠学,杨萱.能源价格改革对碳减排影响效应研究——基于电价与碳排放强度的实证分析[J].价格理论与实践,2019(04):50-53.

[79]邱强,顾尤莉.国际能源价格波动对我国碳排放影响的效应研究——基于岭回归分析[J].国际商务研究,2017,38(05):35-46.

[80]李祎琳.国际直接投资对碳排放的影响——来自中国的经验数据[J].衡水学院学报,2022,24(01):35-41.

[81]张珂.FDI对中国出口贸易隐含碳排放影响研究[J].企业科技与发展,2020(12):19-21.

[82]温忠麟,范息涛,叶宝娟,陈宇帅.从效应量应有的性质看中介效应量的合理性[J].心理学报,2016,48(04):435-443.

[83]梁玲,孙静,岳脉健,耿惠丽.全球能源消费结构近十年数据对比分析[J].世界石油工业,2020,27(03):41-47.

[84]周嘉,时小翠,赵靖宇,王钰萱,孙丽.中国居民直接生活能源消费碳排放区域差异及影响因素分析[J].安全与环境学报,2019,19(03):954-963.

[85]朱勤,彭希哲,陆志明,吴开亚.中国能源消费碳排放变化的因素分解及实证分析[J].资源科学,2009,31(12):2072-2079.

[86]潘栋.中国省域能源碳排放影响因素分析及预测研究[D].北京林业大学,2020.

[87]王瑛,何艳芬.中国省域二氧化碳排放的时空格局及影响因素[J].世界地理研究,2020,29(03):512-522.

[88]刘真真,马远.中国省域碳排放时空格局及其影响因素实证研究[J].新疆农垦经济,2021(02):53-62.

[89]金培振,张亚斌,彭星.技术进步在二氧化碳减排中的双刃效应——基于中国工业35个行业的经验证据[J].科学学研究,2014,32(05):706-716.

[90]吴文洁,刘佩.FDI与碳排放的交互影响——基于281个地级市面板数据的经验分析[J].商业经济,2021(02):117-120+134.

[91]胡剑波,任香,高鹏.中国省际贸易、国际贸易与低碳贸易竞争力的测度研究[J].数量经济技术经济研究,2019,36(09):42-60.