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Research article

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

  • 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.



    Pseudomonas aeruginosa is a significant microorganism involved in urinary, bloodstream, pulmonary, soft tissue and surgical site infections [1],[2]. Antibiotic resistance is one of the most pressing problems in public health and will remain threatening to modern medicine in the coming decades. Because of the increasing abuse of antibiotics in hospitals and the community, some widely used antibiotics are losing their function, and scientists and doctors must develop new antibiotics to overcome this problem. [3],[4]. Metallo-β-lactamaseproducing (MBL) strains to hydrolyze the β-lactam ring of the drug compound, thereby inactivating them. In contrast to serine β-lactamases, MBLs use at least one but more commonly two Zn2+ ions in their active site to catalyze the hydrolysis of β-lactam rings [5],[6]. There are various methods for identifying MBL and NDM-producing strains, which fall into two phenotypic and genotypic groups. Usually, phenotypic methods have low specificity and low speed, and error results [7],[8]. Therefore, it is necessary to use molecular methods along with phenotypic methods. High-resolution melting (HRMA) analysis is one of the most sensitive and precise molecular methods based on real-time PCR [9],[10].

    HRMA is used to characterize bacterial DNA samples according to their dissociation behavior as they transition from double-strand DNA to single-strand DNA with increasing temperature and fluorescence detection [8],[11]. The HRMA is entirely precise warming of the amplicon DNA from around 50 °C up to around 95 °C. During this process, the amplicon's melting temperature is reached, and the two strands of DNA separate or “melt” apart. The concept of HRMA is to monitor this process happening with real-time PCR [12]. This rationale is achieved by using intercalating dyes. The intercalating dye binds explicitly to double-strand DNA and forms the stable fluorescent, and thus one can monitor the relative quantity of the product during DNA amplification in real-time PCR [13]. However, HRMA takes it one step further in its ability to capture much more detail. It has an increased resolving power, as melting curves from different amplicons may be differentiated based on the melt curve's shape even when the melt temperature values are the same [14],[15].

    HRMA has the potential to be a powerful tool in the clinical microbiology laboratory, providing rapid detection of genetic determinants conferring antibiotic resistance to complement current phenotypic antimicrobial susceptibility testing methods [16],[17]. These methods are labor-intensive, expensive and require unique expertise, and the results are difficult to interpret [18]. An accurate, rapid and cost-effective typing scheme is urgently needed for active surveillance and epidemiological investigations [19]. HRMA typing is a compassionate, rapid and cost-effective option for detection purposes [20],[21]. It can perform highly accurate genotyping to a hefty quantity of samples in a short amount of time [8],[22]. HRMA also is a straightforward technology that can perform both the PCR analysis and HRMA in one instrument [20]. This reduces extra expenses, saves time and creates a simpler workflow [16]. It is only necessary to create the PCR reaction volume for each sample to be analyzed, and it eliminates the need for solvents and electrophoresis gels [22].

    Therefore, we try to extend the application of the HRMA assay to this area to help detect the antibiotic resistance in different strains of NDM producing P. aeruginosa. We modified the multiplex HRMA to amplify both the bacteria NDM producing gene and MBL producing gene and analyze its melting curve.

    Subcultures of P. aeruginosa ATCC 15442, P. aeruginosa PAO-1, and P. aeruginosa ATCC 27853 were used from Hamadan Medical University, Microbiology Department microbial bank. Reference strains were cultivated at 37 °C for 24 h in cetrimide agar (Merck, Germany). All strains were kept at −20 °C in TSB containing 20% glycerol. The Ethics Committee approved this study of Hamadan University of Medical Sciences (Code No: IR.UMSHA.REC.1398.573).

    P. aeruginosa DNA extraction was performed using a DNA extraction kit (Qiagen, Germany); the steps were followed according to the kit protocol. DNA concentration was determined using a spectrophotometer (Nanodrop-200, Hangzhou Allsheng Instruments Co., Ltd., China). In this study, the sequencing method used was the dideoxy chain-terminating Sanger method [23].

    Primer sequences were initially set up as outlined in previous studies [24][27] (Table 1). For sensitivity and specificity of primers, nine-fold serial dilutions of 0.5 McFarland DNA (1.5 × 108 CFU/mL) were made (1:1–1, 1:1–2, 1:1–3, 1:1–4, 1:1–5, 1:1–6, 1:1–7, 1:1–8). Serial dilution real-time PCR tested primers' efficiencies for both target genes and reference genes. Standard curves were constructed by the Ct (y-axis) versus log DNA dilution (x-axis). The primer efficiency (E) of one cycle in the exponential phase was calculated according to the equation: E = 10−(1/slope)−1 × 100 [28].

    Briefly, 2 µL (0.5 µM) of each primer, 2 µL of DNA template, 4 µL of EvaGreen, and made up to a final volume of 20 µL using ddH2O. Real-time PCR reactions were performed on the ABI real-time machine (ABI StepOnePlus, USA). The thermal cycles were set for reverse transcription steps (55 °C for 5 min, 95 °C for 10 min, 95 °C for 20 s) followed by PCR steps: 95 °C for 15 s, 59 °C for 30 s, repeated for 40 cycles. The slope value was calculated from serial dilutions for each gene, which was then used to determine the reaction's efficiency [12].

    Table 1.  Oligonucleotide sequences used in this study.
    Target Primer Name Sequence of Primers Melting Tm (±0.5 °C) Accession Number Primer Location Product Size (bp) Ref
    NDM-1 N-1 F: GACCGCCCAGATCCTCAA
    R: CGCGACCGGCAGGTT
    89.57 MN193055.1 71–122 55 [24]
    N-2 F: TTGGCCTTGCTGTCCTTG
    R: ACACCAGTGACAATATCACCG
    76.92 MH168506.2 630–586 85 [25]
    N-3 F: GCGCAACACAGCCTGACTTT
    R: CAGCCACCAAAAGCGATGTC
    82.97 MK371546.1 298–452 155 [23]

    MBL blaSIM F: TACAAGGGATTCGGCATCG
    R: TAATGGCCTGTTCCCATGTG
    85.35 KX452682.1 1–570 570 [27]
    blaVIM F: TCTCCACGCACTTTCATGAC
    R: GTGGGAATCTCGTTCCCCTC
    84.56 NG_068039.1 332–455 124 [26]
    blaSPM F: AAAATCTGGGTACGCAAACG
    R: ACATTATCCGCTGGAACAGG
    86.62 KX452683.1 1–271 271 [27]

     | Show Table
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    The efficiency and the analytical sensitivity of the HRMA-PCR were evaluated by triplicate testing of 9-fold serial dilution series of each of the three reference strains. The Applied Biosystems StepOnePlus real-time PCR system was used to amplify and detect products. The reaction mix was prepared using the following components for each of the samples: 4 µL of Master Mix HRMA (HOT FIREPol EvaGreen HRMA Mix), 1 µM of each respective primer and 12 µL of DMSO (Sigma-Aldrich, USA). The following cycle parameters were used: 2 min at 50 °C, 10 min at 95 °C. Moreover, 40 cycles with denaturing for 15 s at 95 °C and by annealing/elongation for 1 min at 60 °C. Melting curves were generated after each run to confirm a single PCR product (from 60 °C to 95 °C, increasing 1 °C/3 s).

    Data analysis was performed using ABI Thermo Fisher software (release 2018, version 3.0.2) and BioEdit 7.4 software (Caredata, Inc., USA). Normalized and difference plots were generated.

    After 9-fold dilutions, a high CT was observed in the 100 CFU/mL and low CT in the 108 CFU/mL. Moreover, increasing CT values were identified as the inhibitor binding to the DNA, as such binding will reduce the amount of template available for amplification. More comprehensive CT value ranges indicated more binding; likewise, smaller ranges corresponded to less interaction between DNA and inhibitor. The CT values for these cell densities were within the 9 to 40 cycle range in the amplification process, while higher DNA concentrations appeared within 9 to 31 cycles. As seen in Figures 1 and 2, relative to a linear range of each standard curve, melting peaks can be seen for many lower DNA concentrations; however, the concentrations could not be quantified. Therefore, the actual quantitative, linear portions of the calibration curves did not extend as low as the detection limit. Melting curves displayed a single melting Tm: 89.57 °C for the N-1 gene, 76.92 °C for the N-2 gene, 82.97 °C for the N-3 gene, 84.56 °C for the blaVIM gene, 86.62 °C for the blaSIM gene, and 85.35 °C for the blaSPM gene (Figures 1 and 2). Samples containing DNA exhibited positive real-time PCR amplification, and negative controls failed to show amplification. Also, serial dilutions of positive-control DNA amplification curves showed Ct values inversely related to template DNA concentration (Figure 3).

    Figure 1.  Analytical sensitivity of real-time PCR and examples of optimization of primer pairs based on melting curve analysis for NDM primers to detect NDM producing P. aeruginosa strains. The melting curves for each primer pair were investigated: (left) N-1 gene with a melting point of 89.53 ± 0.5 °C, (middle) N-2 gene with a melting point of 76.92 ± 0.5 °C, (right) N-3 gene with a melting point 82.97 ± 0.5 °C. The mean of a: 108; b: 107; c: 106; d: 105; e: 104; f: 103; g: 102; h: 101 and i: 100 CFU/mL of DNA dilutions. Bold black horizontal lines represent the cycle threshold of real-time PCR. One peak with a shoulder corresponds to genomic DNA amplification; no peak corresponds to no amplification. EvaGreen color and single-tube reactions were used in this test. Also, real-time PCR was performed as a single step.
    Figure 2.  Analytical sensitivity of real-time PCR and examples of optimization of primer pairs based on melting curve analysis for MBL primers to detect NDM producing P. aeruginosa strains. The melting curves for each primer pair were investigated: (left) blaVIM gene with a melting point of 84.56 ± 0.5 °C, (middle) blaSPM gene with a melting point of 85.35 ± 0.5 °C, (right) blaSIM gene a melting point of 86.62 ± 0.5 °C. The mean of a: 108; b: 107; c: 106; d: 105; e: 104; f: 103; g: 102; h: 101 and i: 100 CFU/mL of DNA dilutions. Bold black horizontal lines represent the cycle threshold of real-time PCR. One peak with a shoulder corresponds to genomic DNA amplification; no peak corresponds to no amplification. EvaGreen color and single-tube reactions were used in this test. Also, real-time PCR was performed as a single step.
    Figure 3.  Melting curve analysis and analytical specificity of real-time PCR for NDM primers (A) and MBL primers (B) used to detect NDM producing P. aeruginosa strains: (a) blank tube, (b) P. aeruginosa PAO-1 and (c) P. aeruginosa ATCC 27853. One peak with a shoulder corresponds to genomic DNA amplification; no peak corresponds to no amplification. EvaGreen color and single-tube reactions were used in this test. Also, real-time PCR was performed as a single step. A 0.5-McFarland concentration (1.5 x 108 CFU/mL of DNA) was used to determine primer specificity.

    Reaction efficiencies were found to be within the range of 3 to 3.5 when calculated from the standard curves using the ABI Thermo Fisher analysis software (Version 2.3.2) with a formula of E = 10(−1/slope)−1. For the N-1, N-2 and blaVIM primer set, the reaction efficiency reached a value slightly greater than 3, at 3.2, which would suggest the efficiency of 101%. Efficiencies greater than 100% can be obtained. All the investigated dilutions showed low efficiencies: N-3, E = 98.8%; blaSMP, E = 95.588%; blaSIM, E = 96.493 (Figures 1 and 2).

    For the N-1 and blaVIM primer set, the linear range was determined to extend as low as 100 CFU/mL, N-2 was 103 CFU/mL, N-3 was 104 CFU/mL, blaSMP was 102 CFU/mL, and blaSIM was 105 CFU/mL, as indicated by the lowest DNA concentration value on each of the standard curves. Points that caused the curves to deviate from linearity (mostly those with lower concentrations) were excluded (Figures 1 and 2).

    Fluorescence data were analyzed using the tools for HRMA incorporated in the ABI Thermo Fisher analysis software. HRMA PCR amplification curves of samples analyzed for the presence of NDM producing P. aeruginosa are shown in Figures 3, 4, and 5. Difference plots of normalized data show the differences in fluorescence between each sample of DNA. Derivative plots display the rate of fluorescence change; the peak indicates the melting temperature of a sample. All plots displayed a single melting domain, typically between 87.07 °C–87.57 °C for the N-1 gene, 76.42 °C–76.92 °C for the N-2 gene, 82.47 °C–82.97 °C for the N-3 gene, 84.06 °C–84.56 °C for the blaVIM gene, 86.12 °C–86.62 °C for the blaSIM gene and 85.30 °C–85.80 °C for the blaSPM gene, following different product sizes.

    The results of this representative experiment show that all samples containing P. aeruginosa DNA had measurable amplification, as detected by exponential fluorescence (Figure 3), and all the DNA dilutions of NDM producing P. aeruginosa were identified (dilution of 108 to 100 CFU/mL). The N-1 and blaVIM genes in NDM producing P. aeruginosa were detected in all dilutions of DNA. Moreover, N-2, N-3, blaSPM and blaSIM primers can be able to detect bacterial DNA in dilutions of 103 CFU/mL, 104 CFU/mL, 102 CFU/mL and 105 CFU/mL, respectively (Figures 4 and 5).

    Figure 4.  HRMA graphs corresponding to one high-resolution melting analysis of a subset of NDM producing P. aeruginosa strains by N-1 (A), N-2 (B) and N-3 (C) genes. DNA samples from all the dilutions involved in this study were prepared and amplified successfully using the EvaGreen dye-based method in the ABI instrument. Primers' specific melting peaks (Tm) were obtained via HRMA, allowing the differentiation of all investigated β-lactamase enzymes. Due to the positively saturating EvaGreen dye and the HRMA, the resolution accuracy was ±0.1–0.5 °C.
    Figure 5.  HRMA graphs corresponding to one high-resolution melting analysis of a subset of NDM producing P. aeruginosa strain by blaVIM (A), blaSIM (B) and blaSPM (C) genes. DNA samples from all the dilutions involved in this study were prepared and amplified successfully using the EvaGreen dye-based method in the ABI instrument. Primers' specific melting peaks (Tm) were obtained via HRMA, allowing the differentiation of all investigated β-lactamase enzymes. Due to the positively saturating EvaGreen dye and the HRMA, the resolution accuracy was ±0.1 °C–0.5 °C.

    The software automatically analyzed the raw melting curve data and set the starting (pre-melt) and ending (post-melt) fluorescence signals of all data to actual values to aid interpretation and analysis (Figures 4 and 5). The cursors for these two points have defaulted to the ends of the curve. However, these regions were manually adjusted to encompass a representative baseline for the pre-melt and post-melt phases. Widening the normalization regions into the melt phase was avoided to ensure that curves normalize effectively. Moreover, we performed a melt curve analysis of HRMA PCR samples to assess the amplicon's specificity. The results of the HRMA showed a very similar melt peak for all serial dilutions of P. aeruginosa.

    Based on the current study, the efficiency of genes was at least 99.99%, the r2 was >0.99.99, and melt curves yielded single peaks. Interestingly, the Ct vs. DNA relationship's slope varied little across the nine-fold dilutions tested, ranging from −3.589 to −3.955. According to previous studies, this can be justified because short fragments bind less fluorescent and are compensated by a higher primer concentration [29],[30]. However, sometimes the peak height of the short amplicon increases in a different replicate. This problem gets worse in the MCA, such that sometimes we lost the long amplicon even at the primer ratio of 1:1. Furthermore, these results agree with Mentasti et al. [5].

    Moreover, three blaNDM-1 primers with different amplicon length and MBL primers were used to detect NDM producing P. aeruginosa strains. These results indicated that the primers' specificity, with a 0.5 °C error range, could detect NDM producing P. aeruginosa. Andini et al. showed that an accurate analysis of the melting curve could play a significant role in the diagnosis [31]. Ashrafi et al. found that, to obtain the best performance in sophisticated methods such as HRMA, the DNA's melting temperature must be monitored in various dilutions to obtain accurate sensitivity and specificity [21]. Tahmasebi et al. also confirmed that efficiency is probably due to the shorter length of primers' products, which enabled better amplification in PCR [9].

    Based on the current study, Figures 1 and 2 showed that the highest analytical sensitivity and specificity were reported for the detection of antibiotic-resistant strains, as indicated by the lowest DNA concentration value on each of the standard curves. Smiljanic et al. also illustrated that identifying Gram-negative NDM and MBL producing strains is difficult because the resistance to carbapenems in these bacteria is encoded by similar sequences [32]. Thus, using a sensitive and precise method such as HRMA and specific primers could identify strains such as MBL and NDM producing strains. In a study, Ding et al. proposed the PASGNDM699 strain resistance to a wide range of antibiotics. They also confirmed the clinical importance of PASGNDM strains in causing resistant infections [33].

    Based on Figure 4 and Figure 5, DNA dilutions of NDM producing P. aeruginosa strains were identified (dilution of 108 to 100 CFU/mL). The results were different from those obtained by Naas et al. and Smiljanic et al. [32],[34]. Identification of MBL and NDM producing strains has been performed in various studies in Sweden [17], USA [35], Australia [25] and Italy [36] in Gram-negative bacteria by the HRMA method.

    Nevertheless, one of the most important benefits of the multiplex HRMA method is the simultaneous identification of different NDM varieties. Identification of these variants using phenotypic methods has low accuracy and speed. Those methods also require spending much time optimizing. Makena et al. found that the identification of NDM variants by phenotypic methods requires protein stability and is not practical due to the evolution of NDM-producing strains [37].

    According to our results, primers with a short length had the best sensitivity and specificity in the HRMA assay. Słomka et al. demonstrated that when the DNA quality is low, DNA degradation or long DNA breaks during extraction makes the long template harder to amplify [20]. Though, the capacity to monitor PCRs in real-time has revolutionized how PCR is used in the clinical microbiology field. HRMA assay is used to amplify and concurrently quantify a targeted DNA molecule and enables both detection and quantification of DNA. HRMA PCR needs a fluorescent reporter that binds to the finished product and reports its presence by fluorescence. The Eischeid study confirms these results [38].

    In this study, we optimized the HRMA method to identify antibiotic resistance gene variants. We did not use designed primers in this study. The design and optimization of C + G values ​​ in designed primers provide the sensitivity and specificity of HRMA in the simultaneous identification of drug resistance [39]. Thus, the length of the selected primers should be considered to identify bacterial sub-strains. Another limitation of our study was the lack of use of different fluorescent dyes in identifying subtypes. Based on previous studies, the type of dye used significantly affects the sensitivity and specificity of HRMA [40]. Therefore, in different master mixes from different suppliers, calibration is necessary to establish the new Tm data on the reference and clinical strains.

    We demonstrated that the HRMA assay is a rapid and sensitive pre-sequence screening tool that allows the detection of low DNA concentration. Further, our study results showed that NDM and MBL genes' co-existence could be detected using the HRMA method reference strains. Moreover, the HRMA method for identifying NDM and MBL producing strains has high sensitivity and specificity. The present study also confirmed that primer product length and fluorescent dye play critical roles in increasing the sensitivity and specificity of the HRMA assay. However, the selection of the melting temperature range is essential to the analysis, as there needs to be sufficient data both before and following the melting transition to allow reliable normalization of the melting curves.



    [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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