Research article

How do green bonds affect green technology innovation? Firm evidence from China

  • Received: 04 October 2022 Revised: 10 November 2022 Accepted: 28 November 2022 Published: 07 December 2022
  • JEL Codes: G38, G12

  • As an emerging financial tool, green bonds can broaden the financing channels of enterprises and stimulate the green innovation of enterprises. Based on the A-share data of Chinese listed companies from 2012 to 2020, this paper analyzes the impact of green bonds on green technology innovation by using a method of Difference in Difference with Propensity Score Matching (PSM-DID). We found that green bonds can significantly improve enterprise green technology innovation. Its positive impact is attributed to increases in media attention and R&D capital investment and a reduction in financing constraints. Green bonds play a greater role in the green innovation of strong financial constraints enterprises, non-SOEs and large-scale enterprises. Our findings have important reference significance for the improvement of the resource allocation role of green bonds and achievement of sustainable growth.

    Citation: Tao Lin, Mingyue Du, Siyu Ren. How do green bonds affect green technology innovation? Firm evidence from China[J]. Green Finance, 2022, 4(4): 492-511. doi: 10.3934/GF.2022024

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  • As an emerging financial tool, green bonds can broaden the financing channels of enterprises and stimulate the green innovation of enterprises. Based on the A-share data of Chinese listed companies from 2012 to 2020, this paper analyzes the impact of green bonds on green technology innovation by using a method of Difference in Difference with Propensity Score Matching (PSM-DID). We found that green bonds can significantly improve enterprise green technology innovation. Its positive impact is attributed to increases in media attention and R&D capital investment and a reduction in financing constraints. Green bonds play a greater role in the green innovation of strong financial constraints enterprises, non-SOEs and large-scale enterprises. Our findings have important reference significance for the improvement of the resource allocation role of green bonds and achievement of sustainable growth.



    In 2014, an outbreak of Ebola virus (Ebola) decimated many people in Western Africa. With more than 16,000 clinically confirmed cases and approximately 70% mortality cases, this was the more deadly outbreak compared to 20 Ebola threats that occurred since 1976 [1]. In Africa, and particularly in the regions that were affected by Ebola outbreaks, people live close to the rain-forests, hunt bats and monkeys and harvest forest fruits for food [2], [3].

    In [4] develop a SIR type model which, incorporates both the direct and indirect transmissions in such a manner that there is a provision of Ebola viruses with stability and numerical analysis is discussed. A number of mathematical models have been developed to understand the transmission dynamics of Ebola and other infectious diseases outbreak from various aspects [5], [6]. A commonly used model for characterising epidemics of diseases including Ebola is the susceptible-exposed-infectious-recovered (SEIR) model [7], and extensions to this basic model include explicit incorporation of transmission from Ebola deceased hosts [1], [8] or accounting for mismatches between symptoms and infectiousness [9], [10].

    Many researchers and mathematicians have shown that fractional extensions of mathematical integer-order models are a very systematic representation of natural reality [11], [12], [13]. Recently, a non-integer-order idea is given by Caputo and Fabrizio [14]. The primary goal of this article is to use a fresh non-integer order derivative to study the model of diabetes and to present information about the diabetes model solution's uniqueness and existence using a fixed point theorem [15]. Atangana and Baleanu [16] then proposed another non-singular derivative version using the Mittag Leffler kernel function. In many apps in the actual globe, these operators have been successful [17], [18], [19]. The few existing works [4], [8], [9], [20] on the mathematical modeling tells transmission of the virus and spread of Ebola virus on the population of human. The classical settings of mathematical studies tells about spread of EVD, such as SI model, SIR model, SEIR model [4], SEIRD model, or SEIRHD model. World medical association invented medicines for Ebola virus. Quantitative approaches and obtaining an analysis of the reproduction number of Ebola outbreak were important modeling for EVD epidemics. Demographic data on Ebola risk factors and on the transmission of virus were studied through the household structured epidemic model [4], [21]. Predications, different valuable insights, personal and genomic data for EVD was reported and discovered through mathematical models [22], [23]. In [24], the authors observed spread that follows a fading memory process and also shows crossover behaviour for the EVD. They captured this kind of spread using differential operators that posses crossover properties and fading memory using the SIRDP model in [4]. They also analyzed the Ebola disease dynamic by considering the Caputo, Caputo-Fabrizio, and Atangana-Baleanu differential operators.

    In this paper, we developed fractional order Ebola virus model by using the Caputo method of complex nonlinear differential equations. Caputo fractional derivative operator β ∈ (0,1] works to achieve the fractional differential equations. Laplace with Adomian Decomposition Methodsuccessfully solved the fractional differential equations. Ultimately, numerical simulations are also developed to evaluate the effects of the device parameter on spread of disease and effect of fractional parameter β on obtained solution which are also assessed by tabulated results.

    The classical model for Ebola virus model is given in [4], we developed the fractional order Ebola virus model in the followings equations

    Dφ1S(t)=Π(β1I+β2D+λP)SµSDφ2I(t)=(β1I+β2D+λP)S(µ+δ+γ)IDφ3R(t)=γIµRDφ4D(t)=(µ+δ)IbDDφ5P(t)=σ+ξI+αDηP
    with initial conditions
    S(0)=N1,I(0)=N2,R(0)=N3,D(0)=N4,P(0)=N5
    Where S(t) represent the susceptible individuals, I(t) the individuals infected, R(t) the individuals recovered from the EVD, D(t) the individual that died with the Ebola virus and P(t) in the virus concentration in the environment. The susceptible human population is replenished by a constant recruitment at rate Pi. susceptible individuals S may acquire infection after effective contacts β1 with infectious and β2 is effective contact rate of deceased human individuals. They can also catch the infection through contact with a contaminated environment at rate λ. Infectious individuals I experience an additional death due to the disease at rate δ and they are recovered at rate γ. Deceased human individuals can be buried directly during funerals at rate b. Susceptible, infectious and recovered individuals die naturally at rate µ. η, ξ, α, represent the decay rate, shedding rate of infected, and shedding rate of deceased, respectively. The recruitment rate of the Ebola virus in the environment expressed as σ.

    Here system (2.1) is analyzed qualitatively analyzed for feasibility and numerical solution at disease free and endemic equilibrium point. For this purpose, we used

    Dφ1S(t)=Dφ2I(t)=Dφ3R(t)=Dφ4D(t)=Dφ5P(t)=0
    in system (1). For disease free equilibrium, we have E = (π/µ,0,0,0,0) and endemic equilibrium is
    E*=(S*,I*,R*,D*,P*),
    where
    S*=πµR0;I*=π(R01)(µ+δ+γ)R0;R*=πγ(R01)µ(µ+δ+γ)R0;D*=π(µ+δ)(R01)b(µ+δ+γ)R0
    P*=π(bξ+αδ+αµ)(R01)bη(µ+δ+γ)R0
    is endemic equilibria of the system (1). Where reproductive number is
    R0=ηπ(bβ1+β2(µ+δ))+λπ(bξ+αδ+αµ)bηµ(µ+δ+γ)

    Theorem. 1 There is a unique solution for the initial value problem given in system (2.1), and the solution remains in R5, x ≥ 0.

    Proof: We need to show that the domain R5, x ≥ 0 is positively invariant. Since

    Dφ1S(t)|S=0=Π0Dφ2I(t)|I=0=(β1I+β2D+λP)S0Dφ3R(t)|R=0=γI0Dφ4D(t)|D=0=(µ+δ)I0Dφ5P(t)|P=0=σ+ξI+αD0
    Hence the solution lies in feasible domain, so the uniqueness and solution of the system exists.

    Consider the fractional-order Ebola virus model (2.1), by using Caputo definition with Laplace transform, we have

    {Dφ1S(t)}=Π{1}β1{IS}β2{DS}λ{PS}µ{S}{Dφ2I(t)}=β1{IS}+β2{DS}+λ{PS}(µ+δ+γ){I}{Dφ3R(t)}=γ{I}µ{R}{Dφ4D(t)}=(µ+δ){I}b{D}{Dφ5P(t)}=σ{1}+ξ{I}+α{D}η{P}
    Sφ1{S(t)}Sφ11S(0)=Π{1}β1{IS}β2{DS}λ{PS}µ{S}Sφ2{I(t)}Sφ21I(0)=β1{IS}+β2{DS}+λ{PS}(µ+δ+γ){I}Sφ3{R(t)}Sφ31R(0)=γ{I}µ{R}Sφ4{D(t)}Sφ41D(0)={µ+δ}{I}b{D}Sφ5{P(t)}Sφ51P(0)=σ{1}+ξ{I}+α{D}η{P}
    by using the initial conditions (2.2), we get
    {S(t)}=N1S+ΠSφ1+1β1Sφ1{IS}β2Sφ1{DS}λSφ1{PS}µSφ1{S}{I(t)}=N2S+β1Sφ2{IS}+β2Sφ2{DS}+λSφ2{PS}µ+δ+γSφ2{I}{R(t)}=N3S+γSφ3{I}µSφ3{R}{D(t)}=N4S+µ+δSφ4{I}bSφ4{D}{P(t)}=N5S+σSφ5+1+ξSφ5{I}+αSφ5{D}ηSφ5{P}
    We have followings infinite series solution
    S=k=0Sk,I=k=0Ik,R=k=0Rk,D=k=0Dk,P=k=0Pk
    The nonlinearity IS, DS and PS can be written as
    IS=k=0Ak,DS=k=0Bk,PS=k=0Ck
    where Ak, Bk and Ck is called the Adomian polynomials. We have the followings results
    {S0}=N1S+ΠSφ1+1,{I0}=N2S,{R0}=N3S,{D0}=N4S,{P0}=N5S+σSφ5+1
    Similarly, we have
    {S1}=β1Sφ1{A0}β2Sφ1{B0}λSφ1{C0}µSφ1{S0},...{Sk+1}=β1Sφ1{Ak}β2Sφ1{Bk}λSφ1{Ck}µSφ1{Sk}
    {I1}=β1Sφ2{A0}+β2Sφ2{B0}+λSφ2{C0}µ+δ+γSφ2{I0},...{Ik+1}=β1Sφ2{Ak}+β2Sφ2{Bk}+λSφ2{Ck}µ+δ+γSφ2{Ik}
    {R1}=γSφ3{I0}µSφ3{R0},...{Rk+1}=γSφ3{Ik}µSφ3{Rk}
    {D1}=µ+δSφ4{I0}bSφ4{D0},...{Dk+1}=µ+δSφ4{Ik}bSφ4{Dk}
    {P1}=ξSφ5{I0}+αSφ5{D0}ηSφ5{P0},...{Pk+1}=ξSφ5{Ik}+αSφ5{Dk}ηSφ5{Pk}

    We get the followings generalized form for analysis and numerical solution.

    {Sk+1}=β1Sφ1{Ak}β2Sφ1{Bk}λSφ1{Ck}µSφ1{Sk}
    {Ik+1}=β1Sφ2{Ak}+β2Sφ2{Bk}+λSφ2{Ck}µ+δ+γSφ2{Ik}
    {Rk+1}=γSφ3{Ik}µSφ3{Rk}
    {Dk+1}=µ+δSφ4{Ik}bSφ4{Dk}
    {Pk+1}=ξSφ5{Ik}+αSφ5{Dk}ηSφ5{Pk}

    The results of fractional order model (2.1) is represented in followings tables and graphs.

    Table 1.  Numerical solution of S(t) with at different fractional values ϕ.
    t ϕ = 1 ϕ = 0.9 ϕ = 0.8 ϕ = 0.5
    1 39.6912 39.5848 39.51 39.4383
    1.5 39.3299 39.1117 39.0235 39.1181
    3 37.2519 36.2268 36.264 37.5584
    4.5 32.0652 29.2568 30.0105 34.514
    6 19.0984 14.0068 17.2162 29.3074

     | Show Table
    DownLoad: CSV
    Table 2.  Numerical solution of I(t) with at different fractional values ϕ.
    t ϕ = 1 ϕ = 0.9 ϕ = 0.8 ϕ = 0.7
    1 10.4879 10.6026 10.6542 10.7698
    2 12.052 11.9378 11.8579 11.5689
    4 13.2768 12.5387 12.2499 11.8581
    6 8.7256 9.97166 10.5704 12.7973
    8 5.0464 11.4457 13.7013 19.4353

     | Show Table
    DownLoad: CSV
    Table 3.  Numerical solution of R(t) with at different fractional values ϕ.
    t ϕ = 1 ϕ = 0.9 ϕ = 0.8 ϕ = 0.7
    2 20.504 20.5195 20.5331 20.5414
    4 21.952 21.8288 21.6815 21.509
    6 25.448 24.6081 23.8154 23.076
    8 32.096 29.4003 27.1489 25.2921
    10 43 36.6889 31.8489 28.1877

     | Show Table
    DownLoad: CSV
    Table 4.  Numerical solution of D(t) with at different fractional values ϕ.
    t ϕ = 1 ϕ = 0.9 ϕ = 0.8 ϕ = 0.7
    0.5 10.5931 10.6768 10.7803 10.9131
    1 11.3486 11.5116 11.7127 11.9551
    1.5 12.531 12.8083 13.1141 13.4308
    2 14.4048 14.7773 15.124 15.4064

     | Show Table
    DownLoad: CSV
    Table 5.  Numerical solution of P(t) with at different fractional values ϕ.
    t ϕ = 1 ϕ = 0.95 ϕ = 0.9 ϕ = 0.85
    1 5.67835 5.6959 5.68235 5.7302
    2 6.4746 6.46707 6.45834 6.44629
    4 8.788 8.62982 8.47227 8.30553
    6 12.6746 12.1038 11.562 11.0225
    8 18.8688 17.417 16.0958 14.8435
    10 28.105 25.0658 22.3972 19.9683

     | Show Table
    DownLoad: CSV
    Figure 1.  Simulation of S(t) at different fractional values in time t.
    Figure 2.  Simulation of I(t) at different fractional values in time t.
    Figure 3.  Simulation of R(t) at different fractional values in time t.
    Figure 4.  Simulation of D(t) at different fractional values in time t.
    Figure 5.  Simulation of P(t) at different fractional values in time t.
    Figure 6.  Simulation of S(t) at different fractional values in time t.
    Figure 7.  Simulation of I(t) at different fractional values in time t.
    Figure 8.  Simulation of R(t) at different fractional values in time t.
    Figure 9.  Simulation of D(t) at different fractional values in time t.
    Figure 10.  Simulation of P(t) at different fractional values in time t.

    The objective of our work is to develop a scheme of epidemic fractional Ebola virus model with Caputo fractional derivative also numerical solutions have been obtained by using the Laplace with the Adomian Decomposition Method. The results of fractional order Ebola virus model is presented and convergence results of fractional-order model are also presented to demonstrate the efficacy of the process. The analytical solution of the fractional-order Ebola virus model consisting of the non-linear system of the fractional differential equation has been presented by using the Caputo derivative. To observe the effects of the fractional parameter on the dynamics of the fractional-order model (2.1), we conclude several numerical simulations varying the values of parameter given in [4]. These simulations reveal that a change in the value affects the dynamics of the model. The numerical solutions at classical as well as different fractional values by using Caputo fractional derivative can be seen in Figures 15 for disease free equilibrium. The rate of susceptible individuals and pathogens decreases by reducing the fractional values to acquire the desired value, whereas the other compartment starts decreasing by increasing the fractional values. The fractional-order model shows the convergence with theoretical contribution and numerical results. The fractional-order parameter values show the impact of increasing or decreasing the disease. Also, we can fix the parameter values where the rate of infection is decrease and the recover rate will increase for some values which are representing in figures and tables. These results can be used for disease outbreak treatment and analysis without defining the control parameters in the model based on fractional values. In general, approaches to fractional-order modeling in situations with large refined data sets and good numerical algorithms may be worth it. The simulation and numerical solutions at classical as well as different fractional values by using Caputo fractional derivative can be seen in Figures 610 for endemic equilibrium as well as in Tables 15. Results in both cases are reliable at fractional values to overcome the outbreak of this epidemic and meet our desired accuracy. Results discuss in [1], [5] for classical model, but our results are on fractional order model, fractional parameters easily use to adjust the control strategy without defining others parameters in the model. Another important feature that plays a critical role in the 2014 EVD outbreaks is traditional/cultural belief systems and customs. For instance, while some individuals in the three Ebola-stricken nations believe that there is no Ebola, control the population or harvest human organs. We conclude that depending on the specific data set, the fractional order model either converges to the ordinary differential equation model and fits data similarly, or fits the data better and outperforms the ODE model.

    We develop a scheme of epidemic fractional Ebola virus model with Caputo fractional derivative for numerical solutions that have been obtained by using the Laplace with the Adomian Decomposition Method. In [24] the use of three different fractional operators on the Ebola disease model suggests that the fractional-order parameter greatly affects disease elimination for the non-integer case when decreasing α. We constructed a numerical solution for the Ebola virus model to show a good agreement to control the bad impact of the Ebola virus for the different period for diseases free and endemic equilibrium point as well. However, in this work, we introduced the qualitative properties for solutions as well as the non-negative unique solution for a fractional-order nonlinear system. It is important to note that the Laplace Adomian Decomposition Method is used for the Ebola virus fractional-order model differential equation framework is a more efficient approach to computing convergent solutions that are represented through figures and tables for endemic and disease-free equilibrium point. Convergence results of the fractional-order model are also presented to demonstrate the efficacy of the process. The techniques developed to provide good results which are useful for understanding the Zika Virus outbreak in our community. It is worthy to observe that fractional derivative shows significant changes and memory effects as compare to ordinary derivatives. This model will assist the public health planar in framing an Ebola virus disease control policy. Also, we will expand the model incorporating determinist and stochastic model comparisons with fractional technique, as well as using optimal control theory for new outcomes.



    [1] Barbieri N, Marzucchi A, Rizzo U (2020) Knowledge sources and impacts on subsequent inventions: Do green technologies differ from non-green ones? Res Policy 49: 103901. https://doi.org/10.1016/j.respol.2019.103901 doi: 10.1016/j.respol.2019.103901
    [2] Baulkaran V (2019) Stock market reaction to green bond issuance. J Asset Manage 20: 331–340. https://doi.org/10.1057/s41260-018-00105-1 doi: 10.1057/s41260-018-00105-1
    [3] Borsatto JMLS, Bazani CL (2021) Green innovation and environmental regulations: A systematic review of international academic works. Environ Sci Pollution Res 28: 63751–63768. https://doi.org/10.1007/s11356-020-11379-7 doi: 10.1007/s11356-020-11379-7
    [4] Broadstock DC, Cheng LT (2019) Time-varying relation between black and green bond price benchmarks: Macroeconomic determinants for the first decade. Financ Res Lette 29: 17–22. https://doi.org/10.1016/j.frl.2019.02.006 doi: 10.1016/j.frl.2019.02.006
    [5] Brown JR, Fazzari SM, Petersen BC (2009) Financing innovation and growth: Cash flow, external equity, and the 1990s R&D boom. J Finance 64: 151–185. https://doi.org/10.1111/j.1540-6261.2008.01431.x doi: 10.1111/j.1540-6261.2008.01431.x
    [6] Dangelico RM, Pujari D (2010) Mainstreaming green product innovation: Why and how companies integrate environmental sustainability. J Bus Ethics 95: 471–486. https://doi.org/10.1007/s10551-010-0434-0 doi: 10.1007/s10551-010-0434-0
    [7] Du K, Cheng Y, Yao X (2021) Environmental regulation, green technology innovation, and industrial structure upgrading: The road to the green transformation of Chinese cities. Energy Econ 98: 105247. https://doi.org/10.1016/j.eneco.2021.105247 doi: 10.1016/j.eneco.2021.105247
    [8] El Ghoul S, Guedhami O, Kim H, et al. (2018) Corporate environmental responsibility and the cost of capital: International evidence. J Bus Ethics 149: 335–361. https://doi.org/10.1007/s10551-015-3005-6 doi: 10.1007/s10551-015-3005-6
    [9] Flammer C (2021) Corporate green bonds. J Financ Econ 142: 499–516. https://doi.org/10.1016/j.jfineco.2021.01.010 doi: 10.1016/j.jfineco.2021.01.010
    [10] Ghisetti C, Quatraro F (2017) Green technologies and environmental productivity: A cross-sectoral analysis of direct and indirect effects in Italian regions. Ecol Econ 132: 1–13. https://doi.org/10.1016/j.ecolecon.2016.10.003 doi: 10.1016/j.ecolecon.2016.10.003
    [11] Hachenberg B, Schiereck D (2018). Are green bonds priced differently from conventional bonds? J Asset Manag 19: 371–383. https://doi.org/10.1057/s41260-018-0088-5 doi: 10.1057/s41260-018-0088-5
    [12] Hao Y, Ba N, Ren S, et al. (2021) How does international technology spillover affect China's carbon emissions? A new perspective through intellectual property protection. Sustain Prod Consump 25: 577–590. https://doi.org/10.1016/j.spc.2020.12.008 doi: 10.1016/j.spc.2020.12.008
    [13] Hao Y, Huang J, Guo Y, et al. (2022) Does the legacy of state planning put pressure on ecological efficiency? Evidence from China. Bus Strateg Environ 5: 1–22. https://doi.org/10.1002/bse.3066 doi: 10.1002/bse.3066
    [14] Hu AG, Jefferson GH (2009) A great wall of patents: What is behind China's recent patent explosion?. J Dev Econ 90: 57–68. https://doi.org/10.1016/j.jdeveco.2008.11.004 doi: 10.1016/j.jdeveco.2008.11.004
    [15] Huang H, Mbanyele W, Wang F, et al. (2022) Climbing the quality ladder of green innovation: Does green finance matter? Technol Forecast Soc 184: 122007. https://doi.org/10.1016/j.techfore.2022.122007 doi: 10.1016/j.techfore.2022.122007
    [16] Huang Z, Liao G, Li Z (2019) Loaning scale and government subsidy for promoting green innovation. Technol Forecast Soc 144: 148–156. https://doi.org/10.1016/j.techfore.2019.04.023 doi: 10.1016/j.techfore.2019.04.023
    [17] Hyun S, Park D, Tian S (2020) The price of going green: the role of greenness in green bond markets. Account Financ 60: 73–95. https://doi.org/10.1111/acfi.12515 doi: 10.1111/acfi.12515
    [18] Jiang Z, Wang Z, Lan X (2021) How environmental regulations affect corporate innovation? The coupling mechanism of mandatory rules and voluntary management. Technol Soc 65: 101575. https://doi.org/10.1016/j.techsoc.2021.101575 doi: 10.1016/j.techsoc.2021.101575
    [19] Keohane NO, Olmstead SM (2016) Economic Efficiency and Environmental Protection. In Markets and the Environment. 11–34. Island Press, Washington, DC. https://doi.org/10.5822/978-1-61091-608-0_2
    [20] Larcker DF, Watts EM (2020) Where's the greenium? J Account Econ 69: 101312. https://doi.org/10.1016/j.jacceco.2020.101312 doi: 10.1016/j.jacceco.2020.101312
    [21] Li F, Xu X, Li Z, et al. (2021) Can low-carbon technological innovation truly improve enterprise performance? The case of Chinese manufacturing companies. J Clean Prod 293: 125949. https://doi.org/10.1016/j.jclepro.2021.125949 doi: 10.1016/j.jclepro.2021.125949
    [22] Li Z, Liao G, Albitar K (2020) Does corporate environmental responsibility engagement affect firm value? The mediating role of corporate innovation. Bus Strateg Environ 29: 1045–1055. https://doi.org/10.1002/bse.2416 doi: 10.1002/bse.2416
    [23] Lin B, Luan R (2020) Do government subsidies promote efficiency in technological innovation of China's photovoltaic enterprises? J Clean Prod 254: 120108. https://doi.org/10.1016/j.jclepro.2020.120108 doi: 10.1016/j.jclepro.2020.120108
    [24] Liu J, Zhao M, Wang Y (2020) Impacts of government subsidies and environmental regulations on green process innovation: A nonlinear approach. Technol Soc 63: 101417. https://doi.org/10.1016/j.techsoc.2020.101417 doi: 10.1016/j.techsoc.2020.101417
    [25] Liu P, Zhao Y, Zhu J, et al. (2022) Technological industry agglomeration, green innovation efficiency, and development quality of city cluster. Green Financ 4: 411–435. https://doi.org/10.3934/gf.2022020 doi: 10.3934/gf.2022020
    [26] Lv C, Shao C, Lee CC (2021) Green technology innovation and financial development: Do environmental regulation and innovation output matter? Energy Econ 98: 105237. https://doi.org/10.1016/j.eneco.2021.105237 doi: 10.1016/j.eneco.2021.105237
    [27] Managi S, Opaluch JJ, Jin D, et al. (2005) Environmental regulations and technological change in the offshore oil and gas industry. Land Econ 81: 303–319. https://doi.org/10.3368/le.81.2.303 doi: 10.3368/le.81.2.303
    [28] Mbanyele W, Huang H, Li Y, et al. (2022) Corporate social responsibility and green innovation: Evidence from mandatory CSR disclosure laws. Econ Lett 212: 110322. https://doi.org/10.1016/j.econlet.2022.110322 doi: 10.1016/j.econlet.2022.110322
    [29] Miao CL, Meng XN, Duan MM, et al. (2020) Energy consumption, environmental pollution, and technological innovation efficiency: taking industrial enterprises in China as empirical analysis object. Environ Sci Pollut Res 27: 34147–34157. https://doi.org/10.1007/s11356-020-09537-y doi: 10.1007/s11356-020-09537-y
    [30] Mughal N, Arif A, Jain V, et al. (2022) The role of technological innovation in environmental pollution, energy consumption and sustainable economic growth: Evidence from South Asian economies. Energy Strateg Rev 39: 100745. https://doi.org/10.1016/j.esr.2021.100745 doi: 10.1016/j.esr.2021.100745
    [31] Rahman S, Moral IH, Hassan M, et al. (2022) A systematic review of green finance in the banking industry: perspectives from a developing country. Green Financ 4: 347–363. https://doi.org/10.3934/gf.2022017 doi: 10.3934/gf.2022017
    [32] Reboredo JC (2018) Green bond and financial markets: Co-movement, diversification and price spillover effects. Energy Econ 74: 38–50. https://doi.org/10.1016/j.eneco.2018.05.030 doi: 10.1016/j.eneco.2018.05.030
    [33] Ren S, Hao Y, Wu H (2021) Government corruption, market segmentation and renewable energy technology innovation: Evidence from China. J Environ Manage 300: 113686. https://doi.org/10.1016/j.jenvman.2021.113686 doi: 10.1016/j.jenvman.2021.113686
    [34] Ren S, Hao Y, Wu H (2022a) Digitalization and environment governance: does internet development reduce environmental pollution? J Environ Plann Manage 3: 1–30. https://doi.org/10.1080/09640568.2022.2033959 doi: 10.1080/09640568.2022.2033959
    [35] Ren S, Liu Z, Zhanbayev R, et al. (2022b) Does the internet development put pressure on energy-saving potential for environmental sustainability? Evidence from China. J Econ Anal 1: 81–101. https://doi.org/10.12410/jea.2811-0943.2022.01.004 doi: 10.12410/jea.2811-0943.2022.01.004
    [36] Sartzetakis ES (2021) Green bonds as an instrument to finance low carbon transition. Econ Chang Restruct 54: 755–779. https://doi.org/10.1007/s10644-020-09266-9 doi: 10.1007/s10644-020-09266-9
    [37] Singh MP, Chakraborty A, Roy M (2016) The link among innovation drivers, green innovation and business performance: empirical evidence from a developing economy. World Review of Science, Technol Sustainable Dev 12: 316–334. https://doi.org/10.1504/wrstsd.2016.10003088 doi: 10.1504/wrstsd.2016.10003088
    [38] Wang F, Wang R, He Z (2021a) The impact of environmental pollution and green finance on the high-quality development of energy based on spatial Dubin model. Resour Policy 74: 102451. https://doi.org/10.1016/j.resourpol.2021.102451 doi: 10.1016/j.resourpol.2021.102451
    [39] Wang J, Chen X, Li X, et al. (2020) The market reaction to green bond issuance: Evidence from China. Pacific-Basin Financ J 60: 101294. https://doi.org/10.1016/j.pacfin.2020.101294 doi: 10.1016/j.pacfin.2020.101294
    [40] Wang P, Dong C, Chen N, et al. (2021b) Environmental Regulation, Government Subsidies, and Green Technology Innovation—A Provincial Panel Data Analysis from China. Int J Environ Res Public Health 18: 11991. https://doi.org/10.3390/ijerph182211991 doi: 10.3390/ijerph182211991
    [41] Wu H, Hao Y, Ren S, et al. (2021a) Does internet development improve green total factor energy efficiency? Evidence from China. Energy Policy 153: 112247. https://doi.org/10.1016/j.enpol.2021.112247 doi: 10.1016/j.enpol.2021.112247
    [42] Wu H, Xue Y, Hao Y, et al. (2021b) How does internet development affect energy-saving and emission reduction? Evidence from China. Energy Econ 103: 105577. https://doi.org/10.1016/j.eneco.2021.105577 doi: 10.1016/j.eneco.2021.105577
    [43] Xie X, Huo J, Zou H (2019) Green process innovation, green product innovation, and corporate financial performance: A content analysis method. J Bus Res 101: 697–706. https://doi.org/10.1016/j.jbusres.2019.01.010 doi: 10.1016/j.jbusres.2019.01.010
    [44] Yang X, Wang W, Su X, et al. (2022) Analysis of the influence of land finance on haze pollution: An empirical study based on 269 prefecture‐level cities in China. Growth Chang 4: 1–22. https://doi.org/10.1016/j.strueco.2020.12.001 doi: 10.1016/j.strueco.2020.12.001
    [45] Yang X, Wu H, Ren S, et al. (2021) Does the development of the internet contribute to air pollution control in China? Mechanism discussion and empirical test. Struct Chang Econ Dyn 56: 207–224. https://doi.org/10.1016/j.strueco.2020.12.001 doi: 10.1016/j.strueco.2020.12.001
    [46] Yao Y, Hu D, Yang C, et al. (2021) The impact and mechanism of fintech on green total factor productivity. Green Financ 3: 198–221. https://doi.org/10.3934/gf.2021011 doi: 10.3934/gf.2021011
    [47] Yeow KE, Ng SH (2021) The impact of green bonds on corporate environmental and financial performance. Managerial Financ 1: 1–20. https://doi.org/10.1108/mf-09-2020-0481 doi: 10.1108/mf-09-2020-0481
    [48] Yii KJ, Geetha C (2017) The nexus between technology innovation and CO2 emissions in Malaysia: evidence from granger causality test. Energy Procedia 105: 3118–3124. https://doi.org/10.1016/j.egypro.2017.03.654 doi: 10.1016/j.egypro.2017.03.654
    [49] Yin S, Zhang N, Li B (2020) Enhancing the competitiveness of multi-agent cooperation for green manufacturing in China: An empirical study of the measure of green technology innovation capabilities and their influencing factors. Sustain Prod Consump 23: 63–76. https://doi.org/10.1016/j.spc.2020.05.003 doi: 10.1016/j.spc.2020.05.003
    [50] Zerbib OD (2019) The effect of pro-environmental preferences on bond prices: Evidence from green bonds. J Bank Financ 98: 39–60. https://doi.org/10.1016/j.jbankfin.2018.10.012 doi: 10.1016/j.jbankfin.2018.10.012
    [51] Zhang D, Zhang Z, Managi S (2019) A bibliometric analysis on green finance: Current status, development, and future directions. Financ Res Lett 29: 425–430. https://doi.org/10.1016/j.frl.2019.02.003 doi: 10.1016/j.frl.2019.02.003
    [52] Zhang W, Li G (2020) Environmental decentralization, environmental protection investment, and green technology innovation. Environ Sci Pollut Res 10: 1–16. https://doi.org/10.1007/s11356-020-09849-z doi: 10.1007/s11356-020-09849-z
    [53] Zhao L, Zhang L, Sun J, et al. (2022) Can public participation constraints promote green technological innovation of Chinese enterprises? The moderating role of government environmental regulatory enforcement. Technol Forecast Soc Chang 174: 121198. https://doi.org/10.1016/j.techfore.2021.121198 doi: 10.1016/j.techfore.2021.121198
    [54] Zheng C, Deng F, Zhuo C, et al. (2022) Green Credit Policy, Institution Supply and Enterprise Green Innovation. J Econ Anal 1: 28–51. https://doi.org/10.12410/jea.2811-0943.2022.01.002 doi: 10.12410/jea.2811-0943.2022.01.002
    [55] Zhou Q, Du M, Ren S (2022) How government corruption and market segmentation affect green total factor energy efficiency in the post-COVID-19 era: Evidence from China. Front Energy Res 10: 1–16. https://doi.org/10.3389/fenrg.2022.878065 doi: 10.3389/fenrg.2022.878065
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