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徐斌老师(厦大)

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一、个人简介

2011年6月毕业于厦门大学,获经济学博士学位;博士专业:统计学;研究方向:计量经济学;主要研究领域:环境经济、能源经济、非参数计量方法、非线性计量模型。现为国际能源经济协会(IAEE)高级会员、全国工业统计学教学研究会理事、中国优选法统筹法与经济数学研究会高级会员。2020年度“全球前2%顶尖科学家”(斯坦福大学发布);“爱思唯尔2021中国高被引学者”。在国内外主流学术期刊《经济研究》、The Energy Journal、Energy Economics、Energy Policy、Renewable and Sustainable Energy Reviews发表论文100余篇,其中ESI高被引论文15篇、SCI一区40篇、TOP期刊论文41篇。主持包括国家自然基金面上项目、国家社科基金项目、省自然基金项目和国家统计局科研项目等课题20余项;参与国家自然基金重点项目、国家社科基金重大项目等20余项。出版专著2部、获省社会科学优秀成果奖3项(一等奖2项、三等奖1项)、省教学成果奖1项(二等奖),指导学生获国家级和省部级优秀论文奖6项(一等奖4项、二等奖2项)。现为50多个国内外学术期刊审稿专家

二、学习和工作主要经历

2021.09—至今 厦门大学 管理学院/中国能源政策研究院

2011.07―2021.05 江西财经大学 统计学院

2016.02―2017.02 澳大利亚莫纳什大学 计量经济和商务统计系 访问学者

2008.09―2011.06 厦门大学 统计学专业 博士/经济学位

2002.09―2005.06 华东交通大学 统计学专业 硕士/经济学位

1998.07―2002.08 安徽省阜阳市颍东区农林局 技术员

三、讲授的课程

本科生课程:微观经济学、计量经济学、概率论与数理统计

硕士和博士研究生:高级资源与环境经济学、多元统计、空间计量经济学、高级计量经济学

四、研究方向

计量经济学、能源经济、环境经济

五、近年来发表论文

2015年发表的论文:

[1] Factors affecting carbon dioxide (CO2) emissions in China's transport sector: a dynamic nonparametric additive regression model. Journal of Cleaner Production, 2015, 101, 311–322. ESI高被引论文.

[2] Carbon dioxide emissions reduction in China's transport sector: A dynamic VAR (vector autoregression) approach. Energy, 2015, 83, 486–495. ESI高被引论文.

[3] How industrialization and urbanization process impacts on CO2 emissions in China: evidence from nonparametric additive regression models. Energy Economics, 2015, 48, 188–202. ESI高被引论文.

[4]财政支农支出、经济增长、收入差距与区域农村居民消费——基于非参数可加模型的实证研究.数理统计与管理, 2015, 34(5):769-783。

2016年发表的论文:

[5] Regional differences of pollution emissions in China: contributing factors and mitigation strategies. Journal of Cleaner Production, 2016, 112, 1454–1463. ESI高被引论文.

[6] Assessing CO2 emissions in China’s iron and steel industry: a dynamic vector autoregression model. Applied Energy, 2016, 161, 375–386. ESI高被引论文.

[7] Regional differences in the CO2 emissions of China's iron and steel industry: Regional heterogeneity. Energy Policy, 2016, 88, 422–434. ESI高被引论文.

[8] Differences in regional emissions in China's transport sector: Determinants and reduction strategies. Energy, 2016, 95, 459–470.

[9] Reducing CO2 emissions in China's manufacturing industry: Evidence from nonparametric additive regression models. Energy, 2016, 101, 161–173.

[10]A dynamic analysis of air pollution emissions in China: Evidence from nonparametric additive regression models. Ecological Indicators, 2016, 63, 346–358.

[11] Reducing carbon dioxide emissions in China's manufacturing industry: a dynamic vector autoregression approach. Journal of Cleaner Production. 2016, 131(9), 594–606.

[12] A quantile regression analysis of China's provincial CO2 emissions: Where does the difference lie? Energy Policy, 2016, 98, 328–342.

2017年发表的论文:

[13] Does the high–tech industry consistently reduce CO2 emissions? Results from nonparametric additive regression model. Environmental Impact Assessment Review 2017, 63, 44–58.

[14] What cause a surge in China's CO2 emissions? A dynamic vector autoregression analysis. Journal of Cleaner Production, 2017, 143, 17–26.

[15] Assessing CO2 emissions in China's iron and steel industry: A nonparametric additive regression approach. Renewable and Sustainable Energy Reviews, 2017, 72, 325-337.

[16] Factors affecting CO2 emissions in China’s agriculture sector: Evidence from geographically weighted regression model. Energy Policy, 2017, 104, 404-414. ESI高被引论文.

[17] Assessing CO2 emissions in China's iron and steel industry: Evidence from quantile regression approach. Journal of Cleaner Production, 2017, 152, 259-270. (通讯作者).

[18] Which provinces should pay more attention to CO2 emissions? Using the quantile regression to investigate China's manufacturing industry. Journal of Cleaner Production, 2017, 164, 980-993. (通讯作者).

[19] Geographical analysis of CO2 emissions in China's manufacturing industry: A geographically weighted regression model. Journal of Cleaner Production, 2017, 166, 628-640.

2018年发表的论文:

[20] What cause large regional differences in PM2.5 pollutions in China? Evidence from quantile regression model. Journal of Cleaner Production, 2018, 174, 447-461. ESI高被引论文.

[21] Investigating the differences in CO2 emissions in the transport sector across Chinese provinces: Evidence from a quantile regression model. Journal of Cleaner Production, 2018, 175, 109-122.

[22] Investigating the role of high-tech industry in reducing China’s CO2 emissions: A regional perspective. Journal of Cleaner Production, 2018, 177, 169-177.

[23] Assessing the development of China’s new energy industry. Energy economics, 2018, 70,116-131. ESI高被引论文.

[24] Growth of industrial CO2 emissions in Shanghai city: Evidence from a dynamic vector autoregression analysis. Energy, 2018, 151, 167-177. (通讯作者).

[25] How to promote the growth of new energy industry at different stages? Energy Policy, 2018, 118, 390-403. (通讯作者).

[26] Factors affecting CO2 emissions in China’s agriculture sector: A quantile regression. Renewable and sustainable energy reviews, 2018, 94, 15-27. (通讯作者).

[27] Do we really understand the development of China's new energy industry? Energy economics, 2018, 74, 733-745.

[28] “诅咒”还是“福音”:资源丰裕程度如何影响中国绿色经济增长?经济研究, 2018, (9):151–167. (通信作者).

2019年发表的论文:

[29]清洁能源发展、二氧化碳减排与区域经济增长.经济研究, 2019(7):188-202.

[30] Can expanding natural gas consumption reduce China's CO2 emissions? Energy Economics, 2019, 81, 393-407.

[31] How to effectively stabilize China's commodity price fluctuations? Energy Economics, 2019, 84, 104544. (通讯作者).

2020年发表的论文:

[32] How to achieve green growth in China's agricultural sector. Journal of Cleaner Production, 2020, 271, 122770.

[33] How does fossil energy abundance affect China's economic growth and CO2 emissions? Science of The Total Environment, 2020, 719, 137503. (通讯作者).

[34] Investigating drivers of CO2 emission in China's heavy industry: A quantile regression analysis. Energy, 2020, 118159.

[35] Large fluctuations of China's commodity prices: Main sources and heterogeneous effects. International Journal of Finance & Economics, 2020, 26(2), 2074-2089.

[36]研发投入、碳强度与区域二氧化碳排放.《厦门大学学报(哲学社会科学版)》,2020, 260(7):70-84.(人大复印资料全文转载).

[37] Effective ways to reduce CO2 emissions from China's heavy industry? Evidence from semiparametric regression models. Energy Economics, 2020, 92, 104974. (通讯作者).

2021年发表的论文:

[38] Investigating spatial variability of CO2 emissions in heavy industry: Evidence from a geographically weighted regression model. Energy Policy, 2021, 149, 112011.

[39] How to efficiently promote distributed energy resources in China: Using a nonparametric econometric method. Journal of Cleaner Production, 2021, 285, 125420.

[40] How to achieve a low-carbon transition in the heavy industry? A nonlinear perspective. Renewable and Sustainable Energy Reviews, 2021, 140, 110708.

[41] Modeling the impact of energy abundance on economic growth and CO2 emissions by quantile regression: Evidence from China. Energy, 2021, 120416. (通讯作者)

[42] Exploring the driving forces of distributed energy resources in China: Using a semiparametric regression model. Energy, 2021, 236, 121452.

[43] A non-parametric analysis of the driving factors of China's carbon prices. Energy Economics, 2021, 105684. (通讯作者)

2022年发表的论文:

[44] Exploring the effective way of reducing carbon intensity in the heavy industry using a semiparametric econometric approach. Energy, 2022, 123066.

[45] Exploring the spatial distribution of distributed energy in China. Energy Economics, 2022, 105828.

[46] Investigating the Determinants of the Growth of the New Energy Industry: Using Quantile Regression Approach. The Energy Journal, 44(2),1-13. 2022,

[47] Assessing the role of environmental regulations in improving energy efficiency and reducing CO2 emissions: Evidence from the logistics industry. Environmental Impact Assessment Review, 2022, 96, 106831.

[48] Assessing the carbon intensity of the heavy industry in China: Using a nonparametric econometric model. Environmental Impact Assessment Review, 2022, 98, 106925.

[49] How to Efficiently Reduce the Carbon Intensity of the Heavy Industry in China? Using Quantile Regression Approach. International Journal of Environmental Research and Public Health, 2022, 19(19), 12865.

六、学术任职

[1] 国际期刊Energy Economics副主编;

[2] 国际期刊Sustainability客座主编;

[3] 国际期刊Frontiers in Environmental Science副主编 (JCR二区);

[4] 国际期刊Frontiers in Energy Research客座主编 (JCR二区);

[5] 国际期刊International Journal of Agricultural Economics副主编;

[6] 国际期刊Agriculture, Forestry and Fisheries编委;

[7] 国际期刊Journal of Reviews on Global Economics副主编。

[8] 中国优选法统筹法与经济数学研究会/高级会员;

[9] 全国工业统计学教学研究理事会/理事;

[10] 中国地理学会会员;

[11] 中国地理学会人文地理专业委员会委员;

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