Transportation infrastructure, as the foundation and pilot industry of the national economy, and its carbon unlocking is one of the key issues concerning China's sustainable development, so it is necessary to explore the emission reduction law of regional transportation infrastructure in the process of economic development. The Yangtze River economic belt, which spans the east, middle, and west regions of China, is the center of China's economic development and an important carrier of regional high-quality development. Therefore, nine provinces and cities in the Yangtze River economic belt (Shanghai, Chongqing, Sichuan, Jiangsu, Zhejiang, Anhui, Jiangxi, Hubei and Hunan) are selected as study cases. Existing studies on carbon unlocking have mostly explored the influence relationship between single factors and outcomes, and the conclusions of the studies mainly rest on whether each factor promotes or inhibits the realization of carbon unlocking, while these influencing factors are often interdependent rather than independent. In this paper, data from nine provinces and cities in the Yangtze River economic belt from 2012 to 2021 are used as sample cases, and constructs an index system of influencing factors by combining the six major conditional elements of green technological innovation, R&D investment degree, government subsidy, scientific and technological support strength, pollution control strength, and environmental regulation from the three dimensions of technology, economy, and institution, and applies the method of fuzzy set qualitative comparative analysis (fsQCA) to explore the impact of multifactor on the carbon unlocking of transportation infrastructure. Using fsQCA to analyze the causality and mechanism of multiple influencing factors on the carbon release of transportation infrastructure, and to form a research report on the carbon release strategy of transportation infrastructure in the Yangtze River economic belt. The study found that (1) none of the factors can be regarded as a necessary condition leading to the result that the Yangtze River economic belt does not have a high carbon emission intensity, and the realization of the control of the carbon emission intensity of the Yangtze River economic belt requires the joint action of all the conditions. (2) Each antecedent condition produces four groupings of carbon emission intensity reduction through synergistic linkage, and the results of these four groupings reflect the multiple strategies and complex mechanisms for realizing the carbon unlocking of transportation infrastructure in the Yangtze River economic belt. These are system-guided strategy, economy-driven strategy, system-economy dual synergy strategy, and economy-technology dual synergy strategy. (3) Green technology innovation plays a prominent role in controlling the carbon emission intensity of the Yangtze River Economic Belt, indicating that the promotion of the technological level is crucial to the carbon unlocking of transportation infrastructure in the Yangtze River economic belt. The research results reveal the complex interaction of multiple factors in the emission reduction and carbon reduction of transportation infrastructure in the Yangtze River economic belt and provide theoretical support for grouping carbon-unlocking strategies for transportation infrastructure in the Yangtze River economic belt. Finally, based on the analysis of the grouping strategy, specific countermeasures and suggestions are proposed to promote the carbon unlocking of the transportation infrastructure in the Yangtze River economic zone, thus helping to realize the dual-carbon goal.
The economic operation of transportation has been accelerated, and growth has been restored to achieve a good start[J]. Finance & Accounting for Communications, 2023(5): 57.
ShenK R, ShiM Y. Providing strong support for Chinese path to modernization by building a country with great transportation strength[J]. China Review of Political Economy, 2023, 14(6): 22-41.
[5]
TalbiB. CO2 emissions reduction in road transport sector in Tunisia[J]. Renewable and Sustainable Energy Reviews, 2017, 69: 232-238.
[6]
BoldriniN O N, DinizC G D L, ZanchettaI T. Evaluation of energy consumption and emissions of carbon dioxide of road transport of Brazil(2016—2026)[J]. Desenvolvimento E Meio Ambiente, 2020, 54: 205-226.
DingL Y. Improve legislation on carbon peaking and carbon neutrality as soon as possible to promote China's green, low-carbon and healthy development[J]. China Engineering Consulting, 2021(3): 18-19.
[9]
NurdiawatiA, UrbanF. Decarbonising the refinery sector: A socio-technical analysis of advanced biofuels, green hydrogen and carbon capture and storage developments in Sweden[J]. Energy Research & Social Science, 2022, 84: 102358.
LiuZ H, XuJ W. Equity and influence factors of China's provincial carbon emissions under the "Dual Carbon" goal[J]. Scientia Geographica Sinica, 2023, 43(1): 92-100.
[12]
YangJ, CaiW, MaM D, et al. Driving forces of China's CO2 emissions from energy consumption based on Kaya-LMDI methods[J]. The Science of the Total Environment, 2020, 711: 134569.
[13]
ChenH Y, YiJ Z, ChenA B, et al. Green technology innovation and CO2 emission in China: Evidence from a spatial-temporal analysis and a nonlinear spatial durbin model[J]. Energy Policy, 2023, 172: 113338.
[14]
LinB Q, MaR Y. Green technology innovations, urban innovation environment and CO2 emission reduction in China: Fresh evidence from a partially linear functional-coefficient panel model[J]. Technological Forecasting and Social Change, 2022, 176: 121434.
[15]
BrownM A. Could the US become a role model for electricity decarbonization?[J]. One Earth, 2021, 4(4): 466-469.
[16]
DongK Y, JiaR W, ZhaoC Y, et al. Can smart transportation inhibit carbon lock-in? The case of China[J]. Transport Policy, 2023, 142: 59-69.
LiH W, YangM J. The "carbon lock-in" problem and the "carbon unlock" governance system in the low-carbon economy[J]. Science & Technology Progress and Policy, 2013, 30(15): 43-47, 48.
YinX, ZhangJ J. Research on the carbon reduction from double lock-out perspective of technology-institution[J]. Journal of Hefei University (Comprehensive ED), 2020, 37(2): 34-41.
XuY Z, GuoJ, LiuS M. An empirical study on Chinese carbon lock-in and unlocking path under the background of low-carbon economy[J]. Soft Science, 2015, 29(10): 33-38.
CaiH Y, XuY Z, ShuangJ P. The study of temporal and spatial evolution characteristics and the effect mechanism of regional carbon lock-in[J]. Journal of Beijing Institute of Technology (Social Sciences Edition), 2016, 18(6): 23-31.
WuY P. The study on formation mechanism of carbon-lock and unlocked strategy in the process of urbanization in Henan[J]. Journal of Henan Normal University (Philosophy and Social Sciences Edition), 2016, 43(3): 73-76.
[27]
ChenY, WangD, ZhuW X, et al. Effective conditions for achieving carbon unlocking targets for transport infrastructure development-joint analysis based on PLS-SEM and NCA[J]. International Journal of Environmental Research and Public Health, 2023, 20(2): 1170.
TanC H, WangX Y, DiaoF, et al. Research on the influence mechanism of users' continuous use intention in the social Q&A community based on the fsQCA method[J]. Research on Library Science, 2023(1): 74-86.
[30]
FissP C. Building better causal theories: A fuzzy set approach to typologies in organization research[J]. Academy of Management Journal, 2011, 54(2): 393-420.
DuY Z, JiaL D. Configuration perspective and qualitative comparative analysis (QCA): A new approach to management research[J]. Management World, 2017(6): 155-167.
[33]
李亮亮. 基于QCA的淮安市排水管道缺陷及其修复的组态研究[D]. 扬州: 扬州大学, 2022.
[34]
LiL L. Study on configuration of drainage pipe defects and repair based on QAC in Huai'an City[D]. Yangzhou: Yangzhou University, 2022.
LiJ, WuH C. Innovation and entrepreneurship efficiency and its improvement path in China's crowd innovation spaces: Based on the two-stage parallel-series network DEA and fsQCA[J]. R&D Management, 2022, 34(3): 66-80.
DongB Y, XuY Z, SunW Y. Research on the carbon unlocking path of green technology innovation from the perspective of "economic restructuring": Moderating effect of environmental regulation[J]. R&D Management, 2023, 35(4): 34-49.
LiuJ, CaoY R, WuH T. The influence of industrial co-agglomeration on regional green innovation[J]. Forum on Science and Technology in China, 2020(4): 42-50.
[41]
柯技. 我国大中型工业企业研发能力明显增强[N]. 中国信息报, 2007-08-02(002).
[42]
KeJ. The R&D capabilities of China's large and medium-sized industrial enterprises have been significantly enhanced[N]. China Information News, 2007-08-02(002).
Dai D M WangY D. Research on coordinated decision-making of carbon emission reduction in multi-cycle supply chain under double carbon goals[J]. Journal of Chongqing Technology and Business University(Natural Science Edition), 2023, 40(3): 1-8.
SunX M, YanS. The driving path for low-carbon transformation of resource-dependent cities under the TOE framework: An fsOCA research based on 108 resource-dependent cities in China [J]. China Industrial Economics, 2023(23): 72-81.
ZhengY, WuH, MengF R. The attention evolution of the government supports enterprise innovation and development: Based on the analysis of the central science and technology policy texts from 1983 to 2019[J]. Journal of Technology Economics, 2023, 42(4): 12-23.
CuiH Y, WangB Z, XuY. Green financial innovation, financial resource allocation and enterprise pollution reduction[J]. China Industrial Economics, 2023(10): 118-136.
TongY, ZhaoZ Y, LiX. Local governments' emphasis on carbon reduction and enterprise digital transformation: Evidence from high energy-consuming listed companies[J]. Collected Essays on Finance and Economics, 2023(12): 82-91.
WangH G, WangY T, XiaoL X. Forecast and analysis of China's industrial CO2 emissions from 2020 to 2060 based on the IO-SDA method[J]. China Environmental Science, 2024, 44(3): 1743-1755.
[55]
RaginC C. Redesigning Social Inquiry[M]. Chicago: University of Chicago Press, 2008.
QuX Y, ZhaoZ X. Research on characteristic factors and multiple promotion paths of China's industrial green total factor productivity based on fsQCA[J]. Operations Research and Management Science, 2022, 31(6): 154-160.
ZhangM, DuY Z. Qualitative comparative analysis(QCA) in management and organization research: Position, tactics, and directions[J]. Chinese Journal of Management, 2019, 16(9): 1312-1323.
[60]
HaoZ, HeG, WangX, et al. Effect route of entrepreneurial ecosystem on rural industry revitalization: A research based on fuzzy set qualitative comparative analysis[J]. Science of Science and Management of S.& T. 2022, 43(1): 57-75.
XiangY, YeY, MiaoT T, et al. Research on the path of agricultural total factor productivity improvement in the Yangtze River economic belt: Based on DEA and fsQCA analysis of 38 cities[J]. Chinese Journal of Agricultural Resources and Regional Planning, 2023, 44(9): 189-202.
WangY P, SongJ N. Research on the improvement path of green total factor productivity in transportation from the perspective of configuration[J/OL]. Soft Science: 1-11[2023-12-07].
FengZ R, ChenC. Diversified path choices for the high-level development of big data industry in China: Configuration analysis based on TOE framework[J]. Journal of Kunming University of Science and Technology (Social Sciences), 2021, 21(3): 57-66.
LiM. Research on the impact of government financial subsidies on the implementation of corporate social responsibility[J]. Communication of Finance and Accounting, 2023(22): 63-68.
WeiQ F, GuoA N, ShenY. The grouping effect of multifactor-driven regional innovation development under the perspective of industrial agglomeration: Taking Chengdu-Chongging economic circle as an example[J/OL]. Soft Science: 1-14[2023-12-21].
ZhangM, LanH L, ChenW H, et al. Research on the antecedent configuration and performance of strategic change[J]. Journal of Management World, 2020, 36(9): 168-186.