EN中文

Acta Geographica Sinica >

2025 , Vol. 80 >Issue 5: 1261 - 1281

DOI: https://doi.org/10.11821/dlxb202505007

Vegetation and Carbon Cycling

Spatial pathways of clean energy technology transfers and emission reduction effects in the Guangdong-Hong Kong-Macao Greater Bay Area

  • ZHOU Yannan , 1 ,
  • HE Ze 2 ,
  • ZHANG Yaxin 3, 4 ,
  • YANG Sirui 1 ,
  • YANG Yu , 5, 6
Expand
  • 1. Business School, University of Shanghai for Science and Technology, Shanghai 200093, China
  • 2. Energy Research Institute, Chinese Academy of Macroeconomics Research, Beijing 100038, China
  • 3. Postdoctoral Research Workstation of China International Consulting Engineering Co., Ltd., Beijing 100048, China
  • 4. School of Environment, Tsinghua University, Beijing 100084, China
  • 5. Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China
  • 6. Key Laboratory of Regional Sustainable Development Modeling, Institute of Geographic Sciences and Natural Resources Research, CAS, Beijing 100101, China

Received date: 2024-02-22

  Revised date: 2025-01-13

  Online published: 2025-05-23

Supported by

National Natural Science Foundation of China(42130712)

National Natural Science Foundation of China(72348003)

National Natural Science Foundation of China(42201196)

Fold

Abstract

Advancing the transfer and application of clean energy technologies is a pivotal strategy for addressing energy-related environmental and climate challenges. The Guangdong-Hong Kong-Macao Greater Bay Area (GBA), a major economic and innovation hub in China, possesses substantial potential in facilitating clean energy technology transfer and reducing carbon emissions. This study examines the spatial dynamics of local, interregional, and international clean energy technology transfers within the GBA, based on patent transfer data from 2010 to 2022. Furthermore, the study employs the STIRPAT (Stochastic Impacts by Regression on Population, Affluence, and Technology) model to assess the impact of these transfers on the region's emission reduction targets. This study reveals the following findings: (1) The scale of local clean energy technology transfers within the GBA exhibits a fluctuating upward trend, predominantly following an "intra-city hub-and-spoke" model. The transfer network has evolved from a single-core to a dual-core and, eventually, to a multi-center configuration. (2) Interregional clean energy technology transfers are increasingly active, narrowing the gap with local transfers. The transfer model has shifted from concentration to diffusion, with external demand transitioning from the Yangtze River Delta to the Beijing-Tianjin-Hebei region. The spatial pattern of outward diffusion has expanded from innovation-intensive cities in the eastern and central regions to western cities such as Haixi, Urumqi, and Karamay. (3) The scale of international clean energy technology transfers remains relatively small, but its activity is gradually increasing, with the Hong Kong-Shenzhen core network engaging with a more diverse array of partners. (4) Clean energy technology transfer has had a significant inhibitory effect on carbon emissions in the GBA, particularly through local and interregional intercity transfers, while the emission reduction effect of international transfers is not yet significant. This study sheds light on the spatial pathways, characteristics, and emission reduction impacts of clean energy technology transfers in the GBA, providing valuable insights for formulating regional low-carbon policies and promoting technological innovation cooperation.

Key words： clean energy technology; technology transfers; spatial pathways; emission reduction effects; Guangdong-Hong Kong-Macao Greater Bay Area

Cite this article

ZHOU Yannan , HE Ze , ZHANG Yaxin , YANG Sirui , YANG Yu . Spatial pathways of clean energy technology transfers and emission reduction effects in the Guangdong-Hong Kong-Macao Greater Bay Area[J]. Acta Geographica Sinica, 2025 , 80(5) : 1261 -1281 . DOI: 10.11821/dlxb202505007

References

Publishing order | Descend order by publishing year | Descend order by cited within
[1]
IEA. CO2 Emission in 2023. Paris: International Energy Agency, 2024.

[2]
IEA. Tracking Energy Innovation: Focus on China. Paris: International Energy Agency, 2022.

[3]
He Kebin. Dual-carbon goals for a new paradigm in the world economy. Beijing: Xinhua News Agency, 2021. https://www.news.cn/politics/2022-01/11/c_1128253817.htm

[贺克斌. “双碳”目标促进世界经济向新的模式发展. 新华网, 2021. https://www.news.cn/politics/2022-01/11/c_1128253817.htm

[4]
Montresor S, Quatraro F. Green technologies and smart specialisation strategies: A European patent-based analysis of the intertwining of technological relatedness and key enabling technologies. Regional Studies, 2020, 54(10): 1354-1365.

DOI

[5]
Mao Xiyan, He Canfei. The new narrative of economic globalization and the new trends in economic geography. Acta Geographica Sinica, 2023, 78(12): 2905-2921.

DOI

[毛熙彦, 贺灿飞. 经济全球化新叙事与中国经济地理学发展新趋势. 地理学报, 2023, 78(12): 2905-2921.]

DOI

[6]
Yang Y, Zhou Y N, Shan Y L, et al. The shift of embodied energy flows among the Global South and Global North in the post-globalisation era. Energy Economics, 2024, 131: 107408. DOI: 10.1016/j.eneco.2024.107408.

[7]
Zheng X, Yu H, Yang L. Technology imports, independent innovation, and China's green economic efficiency: An analysis based on spatial and mediating effect. Environmental Science and Pollution Research, 2022, 29(24): 36170-36188.

[8]
Duffy J, Ralston J. Innovate versus imitate: Theory and experimental evidence. Journal of Economic Behavior & Organization, 2020, 177: 727-751.

[9]
Weko S, Goldthau A. Bridging the low-carbon technology gap? Assessing energy initiatives for the Global South. Energy Policy, 2022, 169: 113192. DOI: 10.1016/j.enpol.2022.113192.

[10]
Duan Dezhong, Jin Hong. Spatiotemporal process and driving factors of green technology diffusion in Chinese cities. Acta Geographica Sinica, 2023, 78(8): 2001-2018.

DOI

[段德忠, 金红. 中国城市绿色技术扩散的时空过程与形成机制. 地理学报, 2023, 78(8): 2001-2018.]

DOI

[11]
Luan Guiqin, Yang Yi, Zhang Yongkun, et al. Analysis of new low-carbon technology diffusion in China. Resource Development & Market, 2011, 27(11): 1043-1046.

[栾贵勤, 杨怡, 张永坤, 等. 我国低碳技术扩散路径研究. 资源开发与市场, 2011, 27(11): 1043-1046.]

[12]
Cao Xia, Zhang Lupeng. Evolutionary game analysis of the diffusion of green technological innovation of enterprises. China Population, Resources and Environment, 2015, 25(7): 68-76.

[曹霞, 张路蓬. 企业绿色技术创新扩散的演化博弈分析. 中国人口·资源与环境, 2015, 25(7): 68-76.]

[13]
Chen Heng, Peng Cheng, Guo Shuang, et al. Two-stage evolution analysis of green technology innovation diffusion based on complex market network. Chinese Journal of Management Science, 2024, 32(3): 135-144.

[陈恒, 彭程, 郭爽, 等. 基于复杂市场网络绿色技术创新扩散的两阶段演化分析. 中国管理科学, 2024, 32(3): 135-144.]

[14]
Dai Liang, Ding Zijun, Ma Haitao, et al. Spatial linkages and endogenous mechanisms of technology transfer in the Guangdong-Hong Kong-Macao Greater Bay Area. Acta Geographica Sinica, 2024, 79(6): 1503-1520.

DOI

[戴靓, 丁子军, 马海涛, 等. 粤港澳大湾区技术转移的空间关联与内生动力. 地理学报, 2024, 79(6): 1503-1520.]

DOI

[15]
Ding J F, Du D B, Duan D Z, et al. A network analysis of global competition in photovoltaic technologies: Evidence from patent data. Applied Energy, 2024, 375: 124010. DOI: 10.1016/j.apenergy.2024.124010.

[16]
Wang C, Huang X, Hu X Q, et al. Trade characteristics, competition patterns and COVID-19 related shock propagation in the global solar photovoltaic cell trade. Applied Energy, 2021, 290: 116744. DOI: 10.1016/j.apenergy.2021.116744.

[17]
Wang Weidong, Lu Na, Zhang Caijing. Low-carbon technology innovation responding to climate change based on perspective of spatial spillover effect. China Population, Resources and Environment, 2018, 28(8): 22-30.

[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. 中国人口·资源与环境, 2018, 28(8): 22-30.]

[18]
Duan Dezhong, Yang Fan, Hu Xuan. Spatial spillover effect of technology transfer in China's city system and its impacts on city economic growth. Science Research Management, 2020, 41(6): 90-97.

[段德忠, 杨凡, 胡璇. 中国城市技术转移的空间溢出效应与经济增长. 科研管理, 2020, 41(6): 90-97.]

[19]
Sun Zhongrui, Fan Jie, Sun Yong, et al. Structural characteristics and influencing factors of spatial correlation network of green science and technology innovation efficiency in China. Economic Geography, 2022, 42(3): 33-43.

DOI

[孙中瑞, 樊杰, 孙勇, 等. 中国绿色科技创新效率空间关联网络结构特征及影响因素. 经济地理, 2022, 42(3): 33-43.]

DOI

[20]
Liu Y Q, Shao X Y, Tang M P, et al. Spatio-temporal evolution of green innovation network and its multidimensional proximity analysis: Empirical evidence from China. Journal of Cleaner Production, 2021, 283: 124649. DOI: 10.1016/j.jclepro.2020.124649.

[21]
Cheng Danya, Zeng Gang. The structural characteristic green technology innovation network in the Yangtze River Delta from a local-transboundary perspective. Human Geography, 2023, 38(5): 79-87.

[程丹亚, 曾刚. 本地—跨界视角下长三角区域绿色技术创新网络结构特征研究. 人文地理, 2023, 38(5): 79-87.]

[22]
Du Debin, Jin Hong, Duan Dezhong. Ecological green integration in Yangtze River Delta from perspective of intercity green technology transfer. Bulletin of Chinese Academy of Sciences, 2022, 37(12): 1770-1782.

[杜德斌, 金红, 段德忠. 绿色技术跨城流动下长三角生态绿色一体化发展研究. 中国科学院院刊, 2022, 37(12): 1770-1782.]

[23]
Li Q X, Du D B, Yu Y J. Scaling and network evolution of technology transfer in US cities. Nature Cities, 2025, 2: 316-326.

[24]
Jin Hong, Duan Dezhong. Spatial and temporal characteristics of intercity green technology flows and emission reduction effects in the Yangtze River Economic Belt. Progress in Geography, 2024, 43(1): 17-32.

DOI

[金红, 段德忠. 长江经济带城际绿色技术流动的时空特征及减排效应研究. 地理科学进展, 2024, 43(1): 17-32.]

DOI

[25]
Gu Gaoxiang, Wang Zheng. Research of carbon abatement by international low carbon technology transfer in the context of the Paris Agreement. China Soft Science, 2018(12): 8-16.

[顾高翔, 王铮. 《巴黎协定》背景下国际低碳技术转移的碳减排研究. 中国软科学, 2018(12): 8-16.]

[26]
Wu Kang, Geng Yirui, Guo Tao. The impact of green technology innovation on carbon emissions from the perspective of urban agglomeration: The moderating effect of human capitals. Journal of Natural Resources, 2024, 39(9): 2121-2139.

DOI

[吴康, 耿一睿, 郭涛. 城市群绿色技术创新对碳排放的影响: 基于人力资本的调节效应. 自然资源学报, 2024, 39(9): 2121-2139 ]

DOI

[27]
Kennedy M, Basu B. Overcoming barriers to low carbon technology transfer and deployment: An exploration of the impact of projects in developing and emerging economies. Renewable and Sustainable Energy Reviews, 2013, 26: 685-693.

[28]
Kouladoum J C, Ngouhouo I, Wendji Miamo C. Wellbeing in Africa: The role of technology transfer. Technology in Society, 2023, 73: 102238. DOI:10.1016/j.techsoc.2023.102238.

[29]
van der Gaast W, Begg K, Flamos A. Promoting sustainable energy technology transfers to developing countries through the CDM. Applied Energy, 2009, 86(2): 230-236.

[30]
Ma H T, Li Y C, Huang X D. Proximity and the evolving knowledge polycentricity of megalopolitan science: Evidence from China's Guangdong-Hong Kong-Macao Greater Bay Area, 1990-2016. Urban Studies, 2021, 58(12): 2405-2423.

[31]
Cai H C, Feng Z J, Zhou W, et al. Understanding the spatial polarization pattern of technology transfer network in the Guangdong-Hong Kong-Macao Greater Bay area. Growth and Change, 2023, 54(1): 4-25.

[32]
Umme H, Cao X B, Ahsan A. Do green technology innovations, financial development, and renewable energy use help to curb carbon emissions? Renewable Energy, 2022, 193: 1082-1093.

[33]
Yan Xiang, Huang Yongchun, Hu Shiliang, et al. How green technology transfer curbs carbon emissions: Empirical evidence from cities in the Yangtze River Delta. Management Review, 2023, 35(8):171-183.

[严翔, 黄永春, 胡世亮, 等. 绿色技术转移何以抑制碳排放: 基于长三角城市的经验证据. 管理评论, 2023, 35(8): 171-183.]

[34]
Pigato M, Black S, Dussaux D, et al. Technology Transfer and Innovation for Low-carbon Development. Washington: World Bank Publications, 2020.

[35]
Meng Wangsheng, Zhang Yang. Natural resource endowment, path selection of technological progress, and green economic growth: An empirical research based on China's provincial panel data. Resources Science, 2020, 42(12): 2314-2327.

DOI

[孟望生, 张扬. 自然资源禀赋、技术进步方式与绿色经济增长: 基于中国省级面板数据的经验研究. 资源科学, 2020, 42(12): 2314-2327.]

DOI

[36]
Dietz T, Rosa E. Rethinking the environmental impacts of population, affluence and technology. Human Ecology Review, 1994, 1(2): 277-300.

[37]
Ehrlich P R, Holdren J P. Impact of population growth: Complacency concerning this component of man's predicament is unjustified and counterproductive. Science, 1971, 171(3977): 1212-1217.

PMID

[38]
Bathelt H, Malmberg A, Maskell P. Clusters and knowledge: Local buzz, global pipelines and the process of knowledge creation. Progress in Human Geography, 2004, 28(1): 31-56.

[39]
Gertler M S. Tacit knowledge and the economic geography of context, or the undefinable tacitness of being (there). Journal of Economic Geography, 2003, 3(1): 75-99.

[40]
Storper M, Venables A J. Buzz: Face-to-face contact and the urban economy. Journal of Economic Geography, 2004, 4(4): 351-370.

[41]
Audretsch D B, Feldman M P. R&D spillovers and the geography of innovation and production. The American Economic Review, 1996, 86(3): 630-640.

[42]
Asheim B T, Coenen L. Knowledge bases and regional innovation systems: Comparing Nordic clusters. Research Policy, 2005, 34(8): 1173-1190.

[43]
Boschma R. Proximity and innovation: A critical assessment. Regional Studies, 2005, 39(1): 61-74.

[44]
Maskell P, Malmberg A. The competitiveness of firms and regions: 'Ubiquitification' and the importance of localized learning. European Urban and Regional Studies, 1999, 6(1): 9-25.

[45]
Tödtling F, Trippl M. One size fits all? Towards a differentiated regional innovation policy approach. Research Policy, 2005, 34(8): 1203-1219.

[46]
Frenken K, Van Oort F, Verburg T. Related variety, unrelated variety and regional economic growth. Regional Studies, 2007, 41(5): 685-697.

[47]
Boschma R, Frenken K. Why is economic geography not an evolutionary science? Towards an evolutionary economic geography. Journal of Economic Geography, 2006, 6(3): 273-302.

[48]
Martin R, Sunley P. Path dependence and regional economic evolution. Journal of Economic Geography, 2006, 6(4): 395-437.

[49]
Asheim B T, Boschma R, Cooke P. Constructing regional advantage: Platform policies based on related variety and differentiated knowledge bases. Regional Studies, 2011, 45(7): 893-904.

[50]
Teng Y H. Indigenous R&D, technology imports and energy consumption intensity: Evidence from industrial sectors in China. Energy Procedia, 2012, 16: 2019-2026.

[51]
Iammarino S, McCann P. Multinationals and Economic Geography:Location, Technology and Innovation. Massachusetts: Edward Elgar Publishing, 2013.

[52]
Malerba F, Orsenigo L. Technological regimes and sectoral patterns of innovative activities. Industrial and Corporate Change, 1997, 6(1): 83-118.

[53]
Khosla R, Sagar A, Mathur A. Deploying low-carbon technologies in developing countries: A view from India's buildings sector. Environmental Policy and Governance, 2017, 27(2): 149-162

[54]
Scott A, Storper M. Regions, globalization, development. Regional Studies, 2003, 37(6/7): 579-593.

[55]
Gielen D, Boshell F, Saygin D, et al. The role of renewable energy in the global energy transformation. Energy Strategy Reviews, 2019, 24: 38-50.

DOI

[56]
Shan Y L, Guan Y R, Hang Y, et al. City-level emission peak and drivers in China. Science Bulletin, 2022, 67(18): 1910-1920.

DOI PMID

[57]
Wang S J, Wang J Y, Chen X J, et al. Impact of international trade on the carbon intensity of human well-being. Environmental Science & Technology, 2023, 57(17): 6898-6909.

[58]
Gao Shuang, Wang Shaojian, Mo Huibin. A comparative study of China's urbanization process and its carbon emission effect from a global perspective. Scientia Geographica Sinica, 2024, 44(2) :204-215.

DOI

[高爽, 王少剑, 莫惠斌. 全球视角下中国城镇化进程及其碳排放效应的比较研究. 地理科学, 2024, 44(2): 204-215.]

DOI

[59]
Wang Shaojian, Su Yongxian, Zhao Yabo. Regional inequality, spatial spillover effects and influencing factors of China's city-level energy-related carbon emissions. Acta Geographica Sinica, 2018, 73(3): 414-428.

DOI

[王少剑, 苏泳娴, 赵亚博. 中国城市能源消费碳排放的区域差异、空间溢出效应及影响因素. 地理学报, 2018, 73(3): 414-428.]

DOI

[60]
Zhou Y N, Poon J, Yang Y. China's CO2 emission intensity and its drivers: An evolutionary Geo-Tree approach. Resources, Conservation and Recycling, 2021, 171: 105630. DOI: 10.1016/j.resconrec.2021.105630.

[61]
Xu B, Lin B Q. How industrialization and urbanization process impact on CO2 emissions in China: Evidence from nonparametric additive regression models. Energy Economics, 2015, 48: 188-202.

[62]
Zhao Rongqin, Huang Xianjin, Yun Wenju, et al. Key issues in natural resource management under carbon emission peak and carbon neutrality targets. Journal of Natural Resources, 2022, 37(5): 1123-1136.

DOI

[赵荣钦, 黄贤金, 郧文聚, 等. 碳达峰碳中和目标下自然资源管理领域的关键问题. 自然资源学报, 2022, 37(5): 1123-1136.]

DOI

[63]
Chen S Y, Jin H, Lu Y L. Impact of urbanization on CO2 emissions and energy consumption structure: A panel data analysis for Chinese prefecture-level cities. Structural change and economic dynamics, 2019, 49: 107-119.

[64]
Hang Y, Wang Q W, Zhou D Q, et al. Factors influencing the progress in decoupling economic growth from carbon dioxide emissions in China's manufacturing industry. Resources, Conservation and Recycling, 2019, 146: 77-88.

[65]
Gu W, Liu D, Wang C, et al. Direct and indirect impacts of high-tech industry development on CO2 emissions: Empirical evidence from China. Environmental Science and Pollution Research, 2020, 27: 27093-27110.

[66]
Wang Shaojian, Xie Zihan, Wang Zehong. The spatiotemporal pattern evolution and influencing factors of CO2 emissions at the county level of China. Acta Geographica Sinica, 2021, 76(12): 3103-3118.

DOI

[王少剑, 谢紫寒, 王泽宏. 中国县域碳排放的时空演变及影响因素. 地理学报, 2021, 76(12): 3103-3118.]

DOI

[67]
Huang C, Zhu Y Y, Ren M, et al. Prospective climate change impacts on China's fossil and renewable power-generation infrastructure: Regional and plant-level analyses. Resources, Conservation and Recycling, 2023, 188: 106704. DOI:10.1016/j.resconrec.2022.106704.

[68]
Zhou Y N, Yang Y, Xia S Y. A novel geographic evolution tree based on econometrics for analyzing regional differences in determinants of Chinese CO2 emission intensity. Journal of Environmental Management, 2022, 305: 114402. DOI:10.1016/j.jenvman.2021.114402.

[69]
Ma Haitao. The theoretical construction and network simulation of intercity innovative relationships in knowledge flow space. Acta Geographica Sinica, 2020, 75(4):708-721.

DOI

[马海涛. 知识流动空间的城市关系建构与创新网络模拟. 地理学报, 2020, 75(4): 708-721.]

DOI

[70]
Storper M. The Regional World:Territorial Development in A Global Economy. New York: Guilford Press, 1997.

[71]
Xu X P, Sheng Y. Productivity spillovers from foreign direct investment: Firm-level evidence from China. World Development, 2012, 40(1): 62-74.

[72]
Keller W. International Trade, Foreign Direct Investment, and Technology Spillovers in Handbook of the Economics of Innovation. Amsterdam: North-Holland, 2010: 793-829.

[73]
Shang Yongmin, Mi Zefeng. Impact of low-carbon technology innovation cooperation in the Yangtze River Delta on green economic growth. China Population, Resources and Environment, 2023, 33(10): 135-145.

[尚勇敏, 宓泽锋. 长三角低碳技术创新合作对绿色经济增长的影响. 中国人口·资源与环境, 2023, 33(10): 135-145.]

[74]
Yin Fengchun, Tian Nannan, Yan Xiang. Re-examining the carbon emission reduction effect of technology talent clustering from the perspective of green technology transfer. Scientific Management Research, 2023, 41(4): 117-124.

[殷凤春, 田楠楠, 严翔. 绿色技术转移视角下科技人才集聚的碳减排效应再检验. 科学管理研究, 2023, 41(4): 117-124.]

[75]
Liu Zhen, Li Wei, Liu Shenghe. Effects of regional population shrinkage on economic growth and the underlying mechanism. Geographical Research, 2024, 43(4): 949-965.

[刘振, 李伟, 刘盛和. 中国区域人口收缩对经济增长的影响及形成机理. 地理研究, 2024, 43(4): 949-965.]

DOI

Options
Outlines

/

〈 〉