With global low-carbon development, green innovation has becoming a key factor for countries to obtain and maintain core competitive advantages. However, the division of scientific research resources often fails to meet the requirements of economic development. Therefore, regional integration cooperation has become an inevitable choice for innovation and development. However, the effect of the impact, direction, and interaction of regional integration policies on green innovation decision-making remains uncertain. To study the synergistic development mechanism between the two, this study constructs a two-layer network model that includes the decision and influence layers and uses a graph neural network model to map the relationship between the two-layer networks. With the help of opinion dynamics model and public goods game model, we draw the following conclusions. (1) Limited communication between regions can encourage all parties to adopt active green innovation strategies. However, excessive communication can lead to disunity of opinions among all parties, which may not be conducive to green innovation and result in an increase in free-rider behavior. (2) Regional integration development can effectively prompt all parties to choose active green innovation strategies. (3) Increasing the breadth of regional integration cooperation is more effective in promoting the active participation of all parties in green innovation, compared to increasing the depth of regional integration cooperation. This study provides theoretical support for decision-making in regional integration and green innovation development.
BalassaB. Toward a theory of economic integration. Toward a theory of economic integration. 1966.
2.
BerkowitzDDeJongDN. Regional integration: an empirical assessment of Russia. J Urban Econ2003; 53: 541–559.
3.
ChungH. Unequal regionalism: regional planning in China and England. Plann Pract Res2015; 30: 570–586.
4.
LiuWDunfordMSongZ,et al.Urban–rural integration drives regional economic growth in Chongqing, Western China. Area Dev Policy2016; 1: 132–154.
5.
CraneBAlbrechtCDuffinKM,et al.China’s special economic zones: an analysis of policy to reduce regional disparities. Reg Stud Reg Sci2018; 5: 98–107.
6.
HwangWG. Uneven economic integration and regional growth: A perspective of developing economies. The University of Texas at Austin, 1996.
7.
KrugmanP. Increasing returns, imperfect competition and the positive theory of international trade. Handbook of International Economics1995; 3: 1243–1277.
8.
LinklaterA. The transformation of political community: Ethical foundations of the post-Westphalian era. Univ of South Carolina Press, 1998.
9.
RedondoJMOlivarGIbarra-VegaD,et al.Modeling for the regional integration of electricity markets. Energy Sustain Dev2018; 43: 100–113.
10.
ZhangXGuoQECheungDMW,et al.Evaluating the institutional performance of the Pearl River Delta integration policy through intercity cooperation network analysis. Cities2018; 81: 131–144.
11.
FengYWuSWuP, et al.Spatiotemporal characterization of megaregional poly-centrality: evidence for new urban hypotheses and implications for polycentric policies. Land Use Policy2018; 77: 712–731.
12.
LiLMaSZhengY,et al.Integrated regional development: comparison of urban agglomeration policies in China. Land Use Policy2022b; 114: 105939.
13.
ShuCXieHJiangJ,et al.Is urban land development driven by economic development or fiscal revenue stimuli in China?Land Use Policy2018; 77: 107–115.
14.
LiKHouYAndersenPS, et al.An ecological perspective for understanding regional integration based on ecosystem service budgets, bundles, and flows: a case study of the Jinan metropolitan area in China. J Environ Manag2022a; 305: 114371.
15.
ZhangKShaoSFanS. Market integration and environmental quality: evidence from the Yangtze river delta region of China. J Environ Manag2020; 261: 110208.
16.
FangCYuD. Urban agglomeration: an evolving concept of an emerging phenomenon. Landsc Urban Plann2017; 162: 126–136.
17.
CaoZDerudderBPengZ. Comparing the physical, functional and knowledge integration of the Yangtze River Delta city-region through the lens of inter-city networks. Cities2018; 82: 119–126.
18.
YeCZhuJLiS, et al.Assessment and analysis of regional economic collaborative development within an urban agglomeration: Yangtze River Delta as a case study. Habitat Int2019; 83: 20–29.
19.
AnsellCGashA. Collaborative governance in theory and practice. J Public Admin Res Theory2008; 18: 543–571.
20.
ZhangK. Is regional integration beneficial to emission reduction. J Fin Res2018; 451: 67–83.
21.
LiuY. Introduction to land use and rural sustainability in China. Land Use Policy2018; 74: 1–4.
22.
KloostermanRCMusterdS. The polycentric urban region: towards a research agenda. Urban Stud2001; 38: 623–633.
23.
FarrellM. Regional integration and cohesion—lessons from Spain and Ireland in the EU. J Asian Econ2004; 14(6): 927–946.
24.
LiJLinB. Does energy and CO2 emissions performance of China benefit from regional integration?Energy Policy2017; 101: 366–378.
25.
HeWWangBWangZ. Will regional economic integration influence carbon dioxide marginal abatement costs? Evidence from Chinese panel data. Energy Econ2018; 74: 263–274.
26.
ShaoSChenYLiK,et al.Market segmentation and urban CO2 emissions in China: evidence from the Yangtze river Delta region. J Environ Manag2019; 248: 109324.
27.
ChenXHuangB. Club membership and transboundary pollution: evidence from the European Union enlargement. Energy Econ2016; 53: 230–237.
28.
ChenYSLaiSBWenCT. The influence of green innovation performance on corporate advantage in Taiwan. J Bus Ethics2006; 67: 331–339.
29.
AcemogluDAghionPBursztynL,et al.The environment and directed technical change. Am Econ Rev2012; 102: 131–166.
30.
SongWYuH. Green innovation strategy and green innovation: the roles of green creativity and green organizational identity. Corp Soc Responsib Environ Manag2018; 25: 135–150.
31.
SunYRazzaqA. Composite fiscal decentralisation and green innovation: imperative strategy for institutional reforms and sustainable development in OECD countries. Sustain Dev2022; 30: 944–957.
32.
LeiXWangYZhaoD,et al.The local-neighborhood effect of green credit on green economy: a spatial econometric investigation. Environ Sci Pollut Res2021; 28: 65776–65790.
33.
LiuYZhuJLiEY, et al. Environmental regulation, green technological innovation, and eco-efficiency: the case of Yangtze river economic belt in China. Technol Forecast Soc Change2020a; 155: 119993.
34.
MeirunTMihardjoLWHaseebM, et al.The dynamics effect of green technology innovation on economic growth and CO2 emission in Singapore: new evidence from bootstrap ARDL approach. Environ Sci Pollut Res2021; 28: 4184–4194.
35.
SaunilaMUkkoJRantalaT. Sustainability as a driver of green innovation investment and exploitation. J Cleaner Prod2018; 179: 631–641.
36.
ParkJPageGW. Innovative green economy, urban economic performance and urban environments: an empirical analysis of US cities. Eur Plann Stud2017; 25: 772–789.
37.
GaoKYuanY. The effect of innovation-driven development on pollution reduction: empirical evidence from a quasi-natural experiment in China. Technol Forecast Soc Change2021; 172: 121047.
38.
YanZZouBDuK,et al.Do renewable energy technology innovations promote China’s green productivity growth? Fresh evidence from partially linear functional-coefficient models. Energy Econ2020; 90: 104842.
39.
XuLFanMYangL,et al.Heterogeneous green innovations and carbon emission performance: evidence at China’s city level. Energy Econ2021; 99: 105269.
40.
ParamatiSRMoDHuangR. The role of financial deepening and green technology on carbon emissions: evidence from major OECD economies. Finan Res Lett2021; 41: 101794.
41.
CaoSNieLSunH, et al.Digital finance, green technological innovation and energy-environmental performance: evidence from China’s regional economies. J Cleaner Prod2021; 327: 129458.
42.
LiuSHouPGaoY, et al. Innovation and green total factor productivity in China: a linear and nonlinear investigation. Environ Sci Pollut Res2020b: 1–22.
43.
ChenXChenGLinM, et al. How does anti-corruption affect enterprise green innovation in China’s energy-intensive industries?. Environ Geochem Health2022; 44: 2919–2942.
44.
ChenWWuY. China’s new environmental protection law and green innovation: evidence from prefecture-level cities. Complexity2021; 2021: 1–13.
45.
EhrhardtGMarsiliMVega-RedondoF. Diffusion and growth in an evolving network. Int J Game Theory2006a; 34: 383–397.
46.
GilSZanetteDH. Coevolution of agents and networks: opinion spreading and community disconnection. Phys Lett A2006; 356: 89–94.
AcemoğluDComoGFagnaniF,et al.Opinion fluctuations and disagreement in social networks. Math Oper Res2013; 38: 1–27.
49.
RamaziPRiehlJCaoM. Networks of conforming or nonconforming individuals tend to reach satisfactory decisions. Proc Natl Acad Sci USA2016; 113: 12985–12990.
50.
ZinoLYeMCaoM. A two-layer model for coevolving opinion dynamics and collective decision-making in complex social systems. Chaos2020; 30: 083107.
51.
YoungHP. The dynamics of social innovation. Proceedings of the National Academy of Sciences2011; 108(supplement_4): 21285–21291.
52.
BoutabaRSalahuddinMALimamN, et al.A comprehensive survey on machine learning for networking: evolution, applications and research opportunities. J Internet Serv Appl2018; 9: 1–99.
53.
KavianiSSohnI. Influence of random topology in artificial neural networks: a survey. ICT Express2020; 6: 145–150.
54.
DuKLiJ. Towards a green world: how do green technology innovations affect total-factor carbon productivity. Energy Policy2019; 131: 240–250.
55.
YangZHuaYCaoY, et al.Network patterns of zhongyuan urban agglomeration in China based on baidu migration data. ISPRS Int J Geoinf2022; 11: 62.
56.
WuZHPanSRChenFW, et al.A comprehensive survey on graph neural networks. Ieee Trans Neur Netw Learn Syst2021; 32: 4–24.
57.
FanXCXiangCCGongLY, et al.Deep learning for intelligent traffic sensing and prediction: recent advances and future challenges. CCF Trans Pervasive Comput Interact2020; 2: 240–260.
58.
ZhouJCuiGZhangZ, et al.Graph Neural Networks: A Review of Methods and Applications. doi: 10.48550/arXiv.1812.08434. 2018.
59.
KlicperaJBojchevskiAGünnemannS. Predict then Propagate: Graph Neural Networks meet Personalized PageRank. Paper presented at the International Conference on Learning Representations (ICLR), 2019. 2019.
60.
HamiltonWLYingRLeskovecJ. Inductive Representation Learning on Large Graphs. 2017.
61.
KingmaDBaJ. Adam: A Method for Stochastic Optimization. Computer Science. 2014.
62.
ZhangZLSabuncuMR. Generalized Cross Entropy Loss for Training Deep Neural Networks with Noisy Labels. Paper presented at the 32nd Conference on Neural Information Processing Systems (NIPS), Montreal, CANADA. 2018.
