Green Technology Innovation Research Articles

How Does Green Bond Issuance Facilitate the Spillover Effect of Green Technology Innovation in Industry? Evidence from China

As the concept of balancing environmental protection and maintaining sustainable economic development has been widely recognized, the green bond is assuming an increasingly significant role within China’s financial market. We utilize the data from China’s A-share listed enterprises that issued bonds in the period 2010 to 2021 and try to examine whether and how green bond issuance facilitates the spillover effect of green technology innovation in industry. The results show that: (1) Green bond issuance can generate a spillover effect, greatly enhancing green technology innovation within the industry. (2) The spillover effect of green technology innovation from green bond issuance within an industry is more pronounced for state-owned enterprises, and relatively weaker for enterprises in Northeast China in the same industry. Relative to non-high-pollution industries, high-pollution industries reinforce the spillover effect. (3) Financing cost and agency cost are important influencing mechanisms for green bond issuance to improve peer enterprises’ level of green technology innovation. Overall, the results provide theoretical support for encouraging the market for green bonds to maintain their development over the long term and for effectively promoting the transformation of the economy and society to a green and low carbon one.

Urban low-carbon governance and ecological efficiency: new evidence from prefecture-level cities in China

IntroductionIn the search for sustainable development, urban eco-development is becoming a core agenda for all countries. China’s low-carbon city pilot (LCCP) policy is an important initiative to promote urban low-carbon governance. And exploring the direction of LCCP policy is an important step towards sustainable urban development.MethodsSuper-SBM is employed to calculate urban ecological efficiency using panel data from 254 prefecture-level cities between 2007 and 2020. The methods of kernel density estimation and spatial Markov chains are applied to the spatial analysis of urban ecological efficiency. The spatial analyses examine the spatiotemporal patterns and dynamic evolving trends of urban ecological efficiency. Additionally, the multiperiod difference-in-differences method is used to assess the impact of the LCCP policy on urban ecological efficiency. On this basis, we apply the method of mechanism analysis to discuss the influence mechanism.ResultsThe results indicate that urban ecological efficiency gradually forms a continuous spatial clustering pattern, although there is a widening tendency in the absolute differences. In the subsequent transfer process, the spatial factor plays a highly significant role. Benchmark regressions and auxiliary robustness tests demonstrate that the LCCP policy is effective in improving urban ecological efficiency. Further analysis shows that the effect of LCCP policies on ecological efficiency can be transmitted by promoting green technology innovation as well as facilitating a low-carbon transition in the energy mix. The above findings indicate the presence of the "Green Innovation Effect" and the "Decoupling Effect".DiscussionThese discoveries enhance the theoretical framework of urban ecology and offer valuable insights for other regions seeking to implement low-carbon urban governance.

Effect of media attention on corporate green technology innovation: mechanism and evidence from China

Effect of media attention on corporate green technology innovation: mechanism and evidence from China

Green Technology Innovation Guiding Policies under the Synergy of Government and Financial Institutions

Guiding enterprises towards green technology innovation (GTI) is a pivotal strategy for mitigating pollution and energy wastage, thereby facilitating the transition towards a green and low-carbon economy. In order to solve the single irrationality of the existing guidance policy and improve its implementation effect, parameters of environmental regulation and green credit policy are hereby introduced to construct a tripartite evolutionary game model of the government, financial institutions, and enterprises. The evolutionary stability of the government and financial institutions in guiding enterprises' GTI is analyzed. Simulation analysis method is used to visualize the evolution path of the game system under different policy options. The study results show that increasing interest rate subsidies can promote the financial institutions and the government to formulate GTI guidance policies. Besides, there is a significant synergistic effect between interest rate subsidies and green credits, which can efficiently foster GTI within enterprises and decrease their innovation costs. Building on this foundation, further increasing the carbon tax rate can lead to a reduction in government subsidy expenditures, improve the evolution rate of the ideal strategy, and expedite the realization of a green, low-carbon transformation.

Preface

Proceeding of The 7th International Symposium on Green Technology for Value Chains (GreenVC) 2023 November 14-15, 2023 Bandung, West Java Indonesia Organized by: Research Center for Environmental and Clean Technology National Research and Innovation Agency (BRIN) The seventh International Symposium on Green Technology for Value Chains (GreenVC) 2023 was held on November 14-15, 2023, as a hybrid event in Bandung, West Java, Indonesia. The fundamental goals of this symposium were to exchange ideas and information, encourage technology development, and strengthen networking and collaboration among all attendees. The symposium was organized by the Research Center for Environmental and Clean Technology, National Research and Innovation Agency (BRIN). The GreenVC 2023 is an annual international symposium that was first held in 2016. This year’s theme is Unlocking a Sustainable Future through Green Technology Innovations. Due to the transition from the pandemic situation, the symposium should be held as a hybrid event, combining virtual and physical, with some attendees joining in person and others joining remotely online. On the positive side, we could gather 9 keynote speakers (5 attend in person and 4 attend virtually), 5 invited speakers (2 attend in person and 3 attend virtually), 102 presenters (46 attend in person and 56 attend virtually), and participants from various places including Japan, Sri Lanka, Australia, Taiwan, Netherlands, Malaysia, Italy, and of course Indonesia. We were gathered to share knowledge and exchange experiences through meaningful discussions on the latest innovations and technologies in environmental issues. The proceeding of GreenVC 2023 presents the original and unpublished results in all areas of interest. In total, 69 papers have been selected to be published based on the originality of the research and the relevance with the symposium topics: environmental remediation and reclamation; green material and sustainable nanotechnology; waste valorization and biorefineries; smart solutions for climate change and environmental monitoring; and water, sanitation, and hygiene. We would like to express our sincere gratitude to our Advisory Board, Scientific Board, Editorial Board, and Scientific Reviewers for their positive contribution to the quality of the articles presented in this proceeding. We also would like to appreciate all participants attending this symposium as well as the Organizing Committee, who made the symposium a success. Dr. Veny Luvita M.T. Chief Editor List of Committee, Main Rundown and Sponsorship are available in this Pdf.

Can Artificial Intelligence Effectively Improve China’s Environmental Quality? A Study Based on the Perspective of Energy Conservation, Carbon Reduction, and Emission Reduction

The “technological dividends” brought by AI development provide a new model for the country to achieve green governance, enhance enterprises’ ability to manage pollutant emissions during production and operations, and create a new driving force for improving environmental quality. In this regard, this paper systematically examines the impact of AI on environmental quality in China by employing provincial panel data spanning from 2000 to 2020. Focusing on energy conservation, carbon reduction, and emissions mitigation, the analysis is conducted through the application of a two-way fixed-effects model and mediation effects model to explore both the effects and the mechanisms of AI’s influence on environmental quality. The findings indicate that the development and implementation of AI contribute positively to China’s efforts in energy conservation, carbon reduction, and emissions mitigation, ultimately leading to an enhancement in environmental quality. This conclusion remains valid after multiple robustness checks. Mechanism tests reveal that the optimization of regional energy structures, advancements in green technological innovation, and upgrades in industrial structures serve as crucial pathways through which AI facilitates energy conservation, carbon reduction, and emissions mitigation. Heterogeneity analysis uncovers a notable “path dependence” effect in China’s AI development; regions characterized by higher material capital investment, more advanced technological market development, and greater levels of marketization experience a relatively more pronounced impact of AI on the enhancement of environmental quality. This study offers direct references and practical insights for countries globally to foster AI development, enhance environmental quality, and advance high-quality economic growth amid the ongoing wave of digital and intelligent transformation.

Research on Carbon Cap Regulation, Retailer Altruistic Preferences, and Green Decision-Making of Manufacturing Enterprises

As manufacturing advances swiftly and public consciousness about low-carbon practices rises, eco-friendly supply chains have seen significant expansion. This study investigates a government-driven green supply chain in two phases, involving a producer and a seller. Four scenario game models are established to determine whether the manufacturer engages in green technology innovation or whether the retailer has altruistic preferences. The Stackelberg game was used to analyze changes in government carbon quota regulations, retail prices of retailers, and manufacturers’ carbon reduction efforts in the context of carbon market trading. Research shows that the government will set looser carbon emission limits for manufacturers when retailers have no altruistic preferences. When carbon prices in the market are low, encouraging manufacturers to invest in green technology innovation enhances social welfare. This study offers essential theoretical backing for the government in crafting carbon quota regulations and aids businesses in making prompt technological innovation choices.

How does urban industrial structure upgrading affect green productivity? The moderating role of smart city development

The concept of the smart city, which has recently gained prominence as a new urbanization model, represents a practical approach to achieving a balance between economic development and environmental protection. This article analyzes the effect of upgrading urban industrial structures on green total factor productivity (GTFP) and addresses a gap in evaluating the mediating role of smart city development. Utilizing panel data from Chinese prefecture-level cities, the study finds that internal advancements and diversification in industrial structures significantly enhance GTFP, while industrial structure rationalization displays a complex, non-linear correlation with GTFP. Moreover, smart city development is found to positively moderate the relationship between industrial structure and productivity, particularly in terms of outer-advancement, diversification, and rationalization. The moderating effect is evident in green technical efficiency rather than green technological innovation. Cluster analysis reveals significant effects in regions characterized by irrational, diversified, and naturally rational industrial structures. Additionally, the study identifies five impact mechanisms facilitated by smart cities, namely ICT, finance development, digitalization, smart governance, and social trust. These conclusions are of material significance to promote high-quality urban development.

How digital finance and green finance can synergize to improve urban energy use efficiency? New evidence from China

Accelerating the greening and digital transformation of the energy industry to enhance energy efficiency is pivotal for constructing a new energy system. The high-quality development of the energy sector necessitates the support of emerging financial forces. This study utilizes panel data from 279 prefecture-level cities spanning from 2011 to 2021, employing mediation and moderation models to verify the synergistic impact of digital finance and green finance on energy efficiency. Findings indicate that the synergy between digital finance and green finance enhances energy efficiency. Mechanism analysis reveals that this synergy promotes energy efficiency through advancements in green and digital technology innovations. Climate policy uncertainty positively moderates the process by which digital finance and green finance synergistically improve energy efficiency, enhancing the intermediary effects of digital and green technologies. Environmental regulations similarly exert positive moderation on the joint impact of digital finance and green finance on energy efficiency, yet their effects vary heterogeneously, with market-based environmental regulatory tools demonstrating superior policy outcomes. Moreover, the impact of the synergy between digital finance and green finance on energy efficiency exhibits heterogeneity across geographic regions, development timelines, resource endowments, and policy trial effects.

Fostering green technology innovation with green credit: Evidence from spatial quantile approach

Green technology is an important path to achieve low-carbon development, and green credit provides financial support for green technology innovation. Existing literature often fails to pay attention to the important role of spatial factors and outliers in green technology innovation. Based on 2005–2022 provincial panel data in China, this paper uses a novel spatial lag quantile model to explore the impact of green credit on green technology innovation and its impact mechanism. The empirical results indicate that green credit exerts a greater positive impact on green technology in the provinces with moderate technical level. Technological innovation has the characteristic of spatial spillover. The spatial spillover of technology contributes more to green technology innovation in the provinces with low- and medium-tech level. This result has been proven even after robustness test of the changes in sample units, and the replacement of core variable values. Further mechanistic analysis demonstrates that banking market structure and enterprise R&D investment both produces the greater impact on green technology innovation in the low-tech provinces such as Qinghai, Ningxia, and Hainan. This article provides policy reference for local governments to formulate green finance policies and promote carbon neutrality strategies.

The dark side of climate policy uncertainty: Hindering energy transition by shaping environmental taxes effectiveness

Climate policy uncertainty (CPU) may have an adverse impact on the environment by interfering with the effectiveness of environmental policies, but there is currently little evidence to support this indirect effect. By incorporating CPU into the transition function, this paper utilizes the panel smooth transition regression (PSTR) to dynamically analyze how CPU affects the relationship between environmental taxes (ETR) and energy transition. When CPU exceeds the threshold, the promoting effect of ETR on energy transition weakens or reverses. The robustness of the main conclusions is demonstrated by establishing a PSTR estimator with the instrumental variable. This paper also constructs a counterfactual scenario, showing that CPU reduces the positive impact of ETR on renewable energy consumption and generation by 7.6% and 3.5%, respectively. Further analysis indicates that this negative effect arises because CPU likely increases investment risk, particularly for long-term green projects, thereby inhibiting the clean energy market and energy-related green technological innovation. Heterogeneity analysis find that the weakening effect of CPU on the effectiveness of ETR is stronger in countries with low energy resource endowment, high energy intensity, and lower economic development levels, underscoring the need for tailored policy approaches. This research emphasizes that for countries with ambitious energy transition goals, climate policy stability is crucial for ensuring the healthy development of environmental taxes policy and renewable energy markets.

Receiver or transmitter? Unlocking the role of green technology innovation in sustainable development, energy, and carbon markets

In the context of sustainable development, the energy crisis and the volatile carbon trading markets have made the integration of green technology increasingly essential. This study explores the spillover effects between green technology innovation (GTI), oil price, clean energy (CE), carbon emissions trading prices (CETP), and sustainable development (SD) using quantile connectedness. The static analysis reveals that the connectedness relationship in extreme quantiles is remarkably more related than the middle quantile. The dynamic results indicate that major events significantly strengthen the overall risk correlation, but spillover effects are asymmetric under different quantiles. Further analysis under extreme market conditions demonstrates that SD and CE have produced a continuous and steady risk spillover to GTI, while CETP increased the risk transmission to GTI following the COVID-19 pandemic. This research underscores the significance of coordinating the development of GTI, energy, carbon markets, and SD by investigating risk contagion mechanisms in various market scenarios. In terms of policy implications, the findings indicate that stable energy system and reasonable carbon prices can stimulate the advancement of GTI.

Green Technology Innovation and Its Impact on Financial Performance with the Moderation of Green Image and Green Subsidies in SMEs in Depok City

This study aims to explore the impact of green technology innovation on the financial performance of SMEs in Depok City, Indonesia. With a focus on green process and product innovations, the research also examines the moderating effects of green image and green subsidies. The study employs Structural Equation Modeling (SEM) to analyze data collected from 387 SMEs, offering insights into the complex relationships between these variables. The results reveal that green process innovation significantly influences green product innovation, which in turn, positively impacts financial performance. The mediating role of green product innovation underscores the importance of integrating process and product innovations for financial success. Additionally, the study finds that a strong green image enhances the positive relationship between green product innovation and financial performance, highlighting the importance of environmental branding. However, contrary to expectations, green subsidies do not significantly moderate the relationship between green product innovation and financial performance.

Driving Sustainable Development through Green Technology Innovations

Driving Sustainable Development through Green Technology Innovations

Determinants and Outcomes of Green Technology Innovation Adoption among Third-Party Logistics Firms in China: A SEM-ANN Analysis

Although the adoption of green innovation has been explored in various contexts, there is still a lack of research on the determinants of green technology innovation adoption (GTIA) in the third-party logistics (3PL) industry of emerging economies and the resulting economic, environmental, operational, and intangible outcomes. This study aims to investigate the influence of different determinants of technological, organizational, and environmental factors in the TOE-DOI framework on the adoption of green technology innovation, as well as the outcomes of such adoption. The study utilizes sample data from 544 Chinese 3PL firms and employs two-stage structural equation modeling and artificial neural network analysis. Partial least squares structural equation modeling (PLS-SEM) explains 85.4% of the variance in green technology innovation adoption, 21% in environmental outcomes, 18.5% in economic outcomes, 20.8% in operational outcomes, and 22.3% in intangible outcomes. The artificial neural network (ANN) model ranks the standardized importance of each predictive variable. The results indicate that institutional pressure is the most significant determinant of GTIA. Additionally, 3PL firms should consider the positive impact of green supplier integration and relative advantage. Complexity does not have a positive impact on GTIA. The longer a company has been established, the more experience and resources it accumulates, and the more opportunities it has to adopt green technology innovation. This study contributes to the existing research on emerging economies and other regions. Furthermore, this is the first study to successfully validate the nonlinear relationship within the Technology-Organization-Environment (TOE) framework and diffusion of innovation (DOI) theory, namely the TOE-DOI framework. The research findings further enhance the current understanding of green technology innovation adoption and its impact. This study provides valuable insights for managers and policymakers in the 3PL industry to achieve various effects, such as environmental, economic, operational, and intangible outcomes.

Efficiency and Driving Factors of Agricultural Carbon Emissions: A Study in Chinese State Farms

Promoting low-carbon agriculture is vital for climate action and food security. State farms serve as crucial agricultural production bases in China and are essential in reducing China’s carbon emissions and boosting emission efficiency. This study calculates the carbon emissions of state farms across 29 Chinese provinces using the IPCC method from 2010 to 2022. It also evaluates emission efficiency with the Super-Slack-Based Measure (Super-SBM model) and analyzes influencing factors using the Logarithmic Mean Divisia Index (LMDI) method. The findings suggest that the three largest carbon sources are rice planting, chemical fertilizers, and land tillage. Secondly, agricultural carbon emissions in state farms initially surge, stabilize with fluctuations, and ultimately decline, with higher emissions observed in northern and eastern China. Thirdly, the rise of agricultural carbon emission efficiency is driven primarily by technological progress. Lastly, economic development and industry structure promote agricultural carbon emissions, while production efficiency and labor scale reduce them. To reduce carbon emissions from state farms in China and improve agricultural carbon emission efficiency, the following measures can be taken: (1) Improve agricultural production efficiency and reduce carbon emissions in all links; (2) Optimize the agricultural industrial structure and promote the coordinated development of agriculture; (3) Reduce the agricultural labor scale and promote the specialization, professionalization, and high-quality development of agricultural labor; (4) Accelerate agricultural green technology innovation and guide the green transformation of state farms. This study enriches the theoretical foundation of low-carbon agriculture and develops a framework for assessing carbon emissions in Chinese state farms, offering guidance for future research and policy development in sustainable agriculture.

Modeling Digital Inclusive Finance and Corporate Green Technology Innovation Towards the Formation of Green Finance and Green Economy Through Green Monetary and Fiscal Policies in 5 Advanced Asian Countries

Facing global problems such as environmental degradation and climate change. Literature analysis shows that the green economy focuses on transformation towards low-carbon development, resource efficiency and population welfare through technology and innovation. Over the past few decades, research on green finance has increased, with many publications demonstrating the importance of green finance in supporting renewable energy projects and climate-resilient urban infrastructure. Despite progress, challenges such as the global economic contraction due to COVID-19 and increasing poverty show the need for a more integrated and sustainable approach in implementing green finance and the green economy

Green horizons: Synergized green technological innovation and green energy consumption on ecological degradation

The ecological environment is at great risk due to economic activities owing to investments, rapid industrialization, and energy resources depletion, which pose significant environmental challenges globally. Recent research has emphasized on the crucial role of green energy consumption (GEN) and green technology innovation in attaining environmental sustainability. However, there are still areas that need to be addressed. This research study investigates the role of GEN, green technology innovation, and foreign direct investment while controlling the effect of economic growth (EGR) and trade openness level on the ecological footprint (EFP) using data from 1999 to 2022 for Pakistan. We used diverse econometric methodologies to ensure the data consistency. We examine the long-term relationships among the study variables using fully modified ordinary least-squares and canonical co-integrating regression. The results revealed that green technology innovation, and GEN emerge as pivotal in reducing environmental degradation. Conversely, foreign direct investment and EGR increased the EFP, validating the pollution haven hypothesis in Pakistan. These findings highlighted the significance of green technology innovation that increased GEN to address ecological degradation. These findings contribute to more extensive discussions on environmental sustainability and development of the green horizon, particularly in the context of developing countries like Pakistan.

THE IMPACT OF ENTERPRISE DIGITAL TRANSFORMATION ON GREEN TECHNOLOGY INNOVATION IN MANUFACTURING: A FACTOR FLOW PERSPECTIVE

Harnessing enterprise digital transformation to drive green technological innovation and achieve carbon reduction goals is an inevitable trend amid carbon reduction efforts. This study analyzed the impact of digital transformation on green technological innovation and the mediating role of innovation factors through a regression analysis model. The results indicate that digital transformation impacted the quantity of green technology innovation significantly more than quality. This impact was mediated by knowledge capital, human capital, physical capital, and innovation consciousness. The positive influence of digital transformation on green technology innovation varied based on corporate ownership nature and geographical location. Furthermore, the negative impact of green technology innovation on corporate value diminished over time. It is imperative for multiple stakeholders, including governments and enterprises, to collaborate in promoting innovation mobility. They should fully leverage the impact of digital transformation to enhance the quality and quantity of green technological innovation.

Digital Economy, Green Dual Innovation and Carbon Emissions

The digital economy serves as a pivotal catalyst for sustainable and eco-friendly development. This study employs a suite of advanced econometric models, including the fixed effects, mediation, threshold and moderation model, to elucidate the intricate dynamics by which the digital economy influences carbon emissions through the lens of green innovation. Building on the existing research on digital economy, green technology innovation and carbon emissions, this paper takes a dual-innovation perspective and divides green technology innovation into disruptive green technology innovation and incremental green technology innovation. And from the government and the public level, it explores how social concerns affect the effect of digital economy on carbon emissions. The analysis is grounded in a comprehensive dataset encompassing a decade of provincial-level data from 2011 to 2021 across China’s 30 provinces. The benchmark regression outcomes indicate the digital economy’s ability to substantially cut down carbon emissions; the threshold effect and mediating effect models demonstrate that a single-threshold effect exists and that disruptive and progressive green technological innovations mediate such ability. Additional research reveals that the digital economy’s impact on carbon emissions could be positively moderated by public and governmental attention. Eastern and western regions in China, as well as those with high levels of foreign investment and low levels of technological transaction activity, are more affected by the digital economy in terms of carbon emission reduction. Our conclusions offer practical recommendations for digital economy’s coordinated advancement and carbon emissions mitigation, and guide local governments to achieve sustainable development goals (SDGs).