Search Results (34)

In the context of mounting concerns over carbon emissions and the need to accelerate the energy transition, green hydrogen has emerged as a strategic solution for decarbonizing hard-to-abate sectors. This paper introduces a methodological innovation by proposing the Green Hydrogen Efficiency Index (GHEI), a unified and quantitative framework that integrates multiple stages of the hydrogen value chain into a single comparative metric. The index encompasses six core criteria: electricity source, water treatment, electrolysis efficiency, compression, end-use conversion, and associated greenhouse gas emissions. Each are normalized and weighted to reflect different performance priorities. Two weighting profiles are adopted: a first profile, which assigns equal importance to all criteria, referred to as the balanced profile, and a second profile, derived using the analytic hierarchy process (AHP) based on structured expert judgment, named the AHP profile. The methodology was developed through a systematic literature review and was applied to four representative case studies sourced from the academic literature, covering diverse configurations and geographies. The results demonstrate the GHEI’s capacity to distinguish the energy performance of different green hydrogen routes and support strategic decisions related to technology selection, site planning, and logistics optimization. The results highlight the potential of the index to contribute to more sustainable hydrogen value chains and advance decarbonization goals by identifying pathways that minimize energy losses and maximize system efficiency. Full article

Against global carbon neutrality goals and China’s “dual carbon” strategy, this study examines how green technology innovation shapes corporate carbon performance through a dual-path mechanism—improving enterprises’ resource utilization efficiency and environmental governance capabilities. Leveraging data from Chinese A-share listed firms (2007–2022) and methods including fixed effects, instrumental variables, and Heckman two-stage models, key findings include: (1) Green technology innovation significantly improves carbon performance. (2) This effect operates through two pathways: enhancing total factor productivity (TFP) and strengthening environmental governance. (3) Green media and investor attention amplify the positive impact of green innovation on carbon performance. (4) The effect remains significant but shows diminishing marginal returns over 1–4 future periods. (5) Non-state-owned enterprises and non-high-carbon industries exhibit more pronounced improvements. This research provides micro-level evidence for “technology-driven low-carbon transformation”, offering theoretical support for policy differentiation and corporate green technology strategies, with practical implications for achieving China’s “dual carbon” objectives. Full article

Amid escalating global climate crises and the urgent imperative to meet the Paris Agreement’s carbon neutrality targets, the steel industry—a leading contributor to global greenhouse gas emissions—confronts unprecedented challenges in driving sustainable industrial transformation through innovative low-carbon steelmaking technologies. This paper examines decarbonization technologies across three stages (source, process, and end-of-pipe) for two dominant steel production routes: the long process (BF-BOF) and the short process (EAF). For the BF-BOF route, carbon reduction at the source stage is achieved through high-proportion pellet charging in the blast furnace and high scrap ratio utilization; at the process stage, carbon control is optimized via bottom-blowing O₂-CO₂-CaO composite injection in the converter; and at the end-of-pipe stage, CO₂ recycling and carbon capture are employed to achieve deep decarbonization. In contrast, the EAF route establishes a low-carbon production system by relying on green and efficient electric arc furnaces and hydrogen-based shaft furnaces. At the source stage, energy consumption is reduced through the use of green electricity and advanced equipment; during the process stage, precision smelting is realized through intelligent control systems; and at the end-of-pipe stage, a closed-loop is achieved by combining cascade waste heat recovery and steel slag resource utilization. Across both process routes, hydrogen-based direct reduction and green power-driven EAF technology demonstrate significant emission reduction potential, providing key technical support for the low-carbon transformation of the steel industry. Comparative analysis of industrial applications reveals varying emission reduction efficiencies, economic viability, and implementation challenges across different technical pathways. The study concludes that deep decarbonization of the steel industry requires coordinated policy incentives, technological innovation, and industrial chain collaboration. Accelerating large-scale adoption of low-carbon metallurgical technologies through these synergistic efforts will drive the global steel sector toward sustainable development goals. This study provides a systematic evaluation of current low-carbon steelmaking technologies and outlines practical implementation strategies, contributing to the industry’s decarbonization efforts. Full article

This study probes the complex interplay between tourism development (TDI), economic growth (GDP), and environmental sustainability, focusing on the ten most influential tourism nations: China, France, Italy, the United Kingdom, Mexico, Germany, Turkey, Spain, the United States, and Russia, covering the time from 1994 to 2023. This study uses feasible generalized least squares (FGLS) and Two-Stage Least Squares (2SLS) together with Driscoll–Kraay (DK) and panel quantile regression (PaQR) to examine the environmental as well as economic effects of TDI combined with trade openness (TOPE), foreign direct investment (FDI), energy prices (EPS), and population density (POPD). All models show that tourism development, indicated by TDI, and economic growth increase carbon emissions, demonstrating these variables’ adverse environmental impact. Energy prices, trade openness, and foreign direct investment lead to decreased emissions because these factors help promote energy-efficient clean technology. Furthermore, GDP growth positively influences TDI, while excessive carbon emissions negatively impact the appeal of tourism. The results indicate the need for sustainable tourism policies and investment in clean energy and green infrastructure, aligned with SDG 9, to foster innovation in energy-efficient practices and infrastructure. The research also supports SDG 13 by advocating climate-resilient tourism models and policies that decouple economic growth from environmental degradation. By adopting various advanced econometric approaches, this study provides strong evidence on the relationship between tourism, the macroeconomy, and environmental results. It offers fresh insights on how to achieve the growth of tourism and climate protection at the world’s top tourist destinations. Full article

As the world accelerates efforts to combat climate change and transition toward a green, low-carbon economy, the new energy vehicle (NEV) industry has become a key driver of carbon reduction. Its ability to innovate efficiently is critical to long-term sustainable development. This study builds on the innovation value chain theory and introduces an evaluation framework that accounts for undesirable outputs such as energy consumption and pollutant emissions. Using a super-efficiency network SBM–Malmquist model and Tobit regression, we analyze the technological innovation efficiency of 272 A-share listed NEV enterprises in China from 2016 to 2023. Expanding beyond traditional overall assessments, we examine efficiency at different stages of the industry chain and find that: (a) overall technological innovation efficiency has declined, mainly due to weak pure technical efficiency, underscoring the need for better R&D management and resource allocation; (b) efficiency varies across the industry chain, with midstream firms performing better than those upstream and downstream, reflecting differences in technological accumulation and market conditions; (c) R&D tax deductions and market competition significantly boost innovation efficiency by creating pressure-driven incentives, while mismatched labor skills, the “welfare dependence” effect of tax incentives and financing constraints hinder progress. By introducing a two-stage innovation efficiency evaluation framework, this study not only pinpoints where efficiency losses occur along the industry chain but also provides empirical insights to guide targeted policy decisions, offering valuable implications for the sustainable growth of the global NEV industry. Full article

As a scarce strategic resource, the efficient utilization of rare earth resources is crucial for ensuring national economic security and promoting sustainable development. AI, the core engine of the Fourth Technological Revolution, provides a favorable opportunity to drive green technological innovation. Green efficiency in technological innovation has not been adequately studied, and the relationship between green efficiency in the rare earth era and AI is still unclear. Based on the above research gap, this study employs the slack-based measure model to perform both static and dynamic evaluations of green efficiency in technological innovation during the technology development and transformation phases of eight listed Chinese rare earth enterprises from 2017 to 2021. This study aims to provide a policy basis for improving the green efficiency of the rare earth industry and the application of AI in the industrial chain. The findings reveal the following: (1) the green efficiency of technological innovation among these rare earth listed enterprises remains low in both phases, with low pure technical efficiency being a key factor contributing to the overall low green technology innovation efficiency; (2) total factor productivity in the technology development phase exhibits a fluctuating upward trajectory while demonstrating a general downward trend in the achievement transformation phase; and (3) the application of AI significantly enhances the green efficiency of technological innovation during the transformation phase, with a more pronounced impact compared to the technology development phase. This study contributes to the existing literature by extending previous research on AI and green efficiency, particularly within the context of the rare earth industry. The empirical results offer valuable policy recommendations for improving the utilization of rare earth resources and enhancing green technological innovation through AI integration. Full article

As an important economic and ecological barrier in China, the Yellow River Basin faces dual challenges of an excessive environmental burden and insufficient innovation efficiency during the process of industrial relocation. This paper divides green innovation into two stages: technological R&D and commercialization, and employs a two-stage data envelopment analysis model to measure the green innovation efficiency of 64 cities in the Yellow River Basin’s urban agglomerations from 2004 to 2021. The results indicate that undertaking regional industrial relocation has a positive impact on green innovation efficiency, but while boosting the efficiency of commercialization, it negatively affects R&D efficiency, leading to an imbalance in green innovation development. Industrial relocation mainly exerts heterogeneous effects on each stage of green innovation efficiency by hindering industrial upgrading, generating dispersive effects, and creating technological spillover effects. Meanwhile, infrastructure development and intellectual property protection play a moderating role. This study provides valuable insights for promoting high-quality development and sustainable green innovation in the Yellow River Basin. Full article

China’s economic development has had adverse effects on the environment. Nevertheless, the Chinese government is optimistic about its ability to achieve a harmonious balance between economic growth and environmental sustainability. To this end, the government promotes, guides, and supports green innovation through the provision of policies and the creation of a conducive environment. Enterprises play a vital role in implementing and advancing green innovation, making them essential to realizing green development through their innovative capabilities. The relationship between government and business acts as a bridge between these two entities, fostering a synergistic effect that is crucial for achieving this objective. This paper conducts a comprehensive analysis of the existing literature on government–business relationships and green innovation, both domestically and internationally. Utilizing data envelopment analysis, it accurately measures the green innovation efficiency of 292 prefectural-level cities in China. The paper then employs a combination of a qualitative comparative analysis and a requisite condition analysis to establish the causal link between government–business relationships and green innovation development. Our detailed analysis of the data has identified three modes of driving green innovation: ‘government service-oriented’, ‘policy support-oriented’, and ‘pro- and clean-oriented’. These modes correspond to the developmental trajectories of China’s first, second, and third–fourth–fifth-tier cities, respectively. This paper offers policy recommendations for the systematic reform and optimization of government–business relationships in China. These recommendations are grounded in the three dimensions of the government–business relationship and consider the current conditions of cities at various stages of economic development. The findings and recommendations presented in this paper can serve as valuable insights for policymakers and businesses alike, providing guidance for future initiatives aimed at fostering a more sustainable economy. Full article

As one of the effective ways to achieve “carbon neutrality”, examining the impact of green finance (GF) on carbon emission efficiency (CE) is of great significance for promoting low-carbon development in China. Moreover, the digital economy is a key catalyst in achieving China’s “dual-carbon” targets, as its “greening” characteristic is considered instrumental in promoting urban low-carbon development. However, the effects of the digital economy (Dig) stage on GF on urban CE have not been sufficiently studied. Using panel data from 276 Chinese cities from 2011 to 2021 and constructing a theoretical model based on the Cobb–Douglas production function, this paper analyzes the impact of GF on urban CE. The empirical results indicate that (1) GF can improve CE, and the two have a positive U-shaped relationship, which is still valid after robustness tests. (2) The heterogeneity results indicate that the impact of GF on CE is more significant in non-resource-based cities, low-carbon pilot cities, and cities with higher financial development levels. (3) GF significantly improves urban CE by driving green technology innovation (Gti) and energy efficiency improvement (Eei). (4) The effects of GF on CE have a dual-threshold effect based on the Dig. When the Dig level is excessively high, the positive effect of GF on urban CE will be weakened. Full article

Agricultural production faces the dual challenge of increasing output while ensuring efficient resource utilization and environmental sustainability amid escalating global climate change and relentless increases in human demand. This study used provincial panel data from China from 2001 to 2022 to address these challenges. It systematically evaluated the dynamic evolution of agricultural green total factor productivity (AGTFP) by selecting “resources” and “energy” as core input factors and adopting a dual-output approach focused on “economic” and “low-carbon” outcomes. This study thoroughly analyzed the synergistic mechanisms of factors such as natural endowment, agricultural technology, economic development, and environmental regulation, exploring their impact on AGTFP enhancement through the innovative application of the dynamic fuzzy-set qualitative comparative analysis (fsQCA) method. There was a significant upward trend in AGTFP across China, indicating notable progress in green agricultural development. Additionally, three pathways promoting AGTFP improvement were identified: resource–economy-driven, technology–policy-guided, and multifactor-synergy. Simultaneously, two modes constraining AGTFP enhancement were uncovered: economy–policy suppression and human capital–economy suppression, highlighting the pivotal role of regional economic development and the conditionality of converting natural resource advantages. Moreover, the contributions of these pathways to AGTFP exhibited notable temporal dynamics. Major economic events, such as the 2008 financial crisis and policy shifts, including the 2012 “Ecological Civilization” strategy, significantly altered the effectiveness of existing configurations. Our analysis of regional heterogeneity revealed distinct geographical patterns, with the resource–economy-driven model predominantly observed in central regions and the technology–policy-guided and multi-factor-synergy models more prevalent in central and eastern regions. These findings highlight the importance of formulating differentiated policies tailored to the specific needs and stages of development in different regions. Specifically, enhancing the synergy between resource management and economic development, optimizing technology–policy integration, and promoting coordinated multisectoral development are critical to fostering sustainable agricultural practices. This research provides crucial empirical evidence for shaping targeted policies that can drive green agricultural development across diverse regional contexts. Full article

Promoting green innovation is an important way to implement the dual carbon strategy and build an innovative country. Based on the panel data of 250 cities in China from 2011 to 2018, this paper constructs a two-way fixed-effect model, an intermediary effect model and a spatial Durbin model, and empirically studies the impact and mechanism of digital finance on urban green innovation. The results show that digital finance can improve the ability of urban green innovation, and its enabling effect mainly comes from improving the financial service model and improving the digital level. However, the role of digital finance in improving the efficiency of green innovation is not significant. Digital finance can promote urban green innovation by promoting the development of the Internet and alleviating the distortion of labor factors. A good environment for innovation will enhance the role of digital finance in promoting green innovation. Through further analysis, the spatial spillover effect of digital finance on green innovation at this stage is dominated by the siphon effect while the “trickle-down” effect is blocked. Full article

To evaluate the effectiveness of the low-carbon innovation development of enterprises, this paper proposes a neutral dynamic network cross-efficiency model and introduces the bootstrap sampling method to correct the model. The model categorizes the low-carbon green innovation R&D activities of enterprises into two distinct stages, as follows: the green R&D stage and the results transformation stage. It then assesses the efficiency of each stage and provides an overall efficiency rating. The model has been applied to a sample of listed Chinese iron and steel enterprises (CISES). The results of the study show that the overall efficiency of low-carbon innovation and development of CISES is on the low side, with the highest efficiency achieved in the green R&D stage, which is less than the lowest efficiency attained in the transformation stage, and most of the enterprises are in the stage of high green R&D and low transformation of the results. The ability of marketization of the R&D results still needs to be strengthened. Full article

Climate change is a challenge facing all countries around the world. In response to the global climate change, China has pledged a two-stage carbon reduction goal of “dual carbon” to realize sustainable development. Industrial structure upgrading driven by green finance is an important way to reduce carbon emissions and achieve sustainable development. In this work, we investigate the impact of green finance on promoting industrial structure upgrading in Jiangsu province. We construct the grey correlation degree and coupling coordination degree model to analyze the relationship between green finance development and industrial structure upgrading with data from 13 prefecture-level cities in Jiangsu province from 2010 to 2021. The results demonstrate that green finance policies inhibit the financing tendencies of high-energy consumption industries and improve the financing difficulties of high-energy enterprises, forcing high-energy industries to transform and realize industrial upgrading. In addition, the improvement in green energy consumption structure and energy production efficiency will promote an improvement in carbon emission efficiency. Moreover, the development of green finance contributes to promoting industrial structure upgrading, putting forward new requirements for the development of green finance as well. Furthermore, the promotion of green finance and low-carbon industries provides a strong driving force for industrial structure upgrading as well as high-quality economic development in Jiangsu province. Therefore, the green finance policy system, as well as innovation in green financial products, needs to be further improved to accelerate industrial structure upgrading. Full article

Industrial enterprises are characterized by significant energy consumption and high emissions. Therefore, the implementation of green innovation by these enterprises is beneficial for promoting sustainable economic development and safeguarding the ecological environment. In this study, a relational two-stage DEA model containing shared inputs and undesired outputs is constructed to evaluate and decompose the green innovation efficiency (GIE) of Chinese industrial enterprises across 30 provinces from 2012 to 2021. Since the objective function of this model is nonlinear, a heuristic search method is employed for its resolution. On the basis of efficiency evaluation, the Gini coefficient, kernel density estimation, and convergence analysis are further employed to investigate the regional disparities and convergence properties in the two-stage efficiency of green innovation. Our findings are as follows: (1) The average GIE of Chinese industrial enterprises demonstrates a fluctuating upward trajectory, with significant regional disparities observed between provinces. (2) Regional disparities in R&D efficiency (RDE) and achievement conversion efficiency (ACE) have diminished in all regions. Super-variable density and interregional differences serve as the primary sources of regional disparities in RDE and ACE, respectively. (3) The presence of absolute and conditional convergence in RDE and ACE is observed across all regions. To improve the GIE of Chinese industrial enterprises, it is imperative to emphasize the heterogeneous impact of economic levels, industrial structure, and the degree of openness across various regions and stages of green innovation. Full article

Using the differential game model, this study examines the impact of the digital economy and regional cooperative innovation on green innovation efficiency. Additionally, based on the two-stage Super-NSBM model, this study evaluates the effects of the digital economy on green innovation efficiency, its spatial spillover effects, and the moderating role of regional cooperative innovation. The findings of the study indicate that (1) the digital economy significantly enhances green innovation efficiency but has negative spatial spillover effects on surrounding regions. (2) Regional cooperative innovation positively moderates the promotional effect of the digital economy on green innovation efficiency. Moreover, the moderating effect exhibits a single-threshold effect. (3) The influence of the digital economy on green innovation efficiency is more significant in regions with advanced industrialization, robust transportation infrastructure, and high R&D intensity. The coordinated development of digital industrialization and governance is crucial for effectively promoting the development of green innovation. Full article