Malmquist Luenberger Research Articles

Exploring the Impact of Ecological Degradation on the Green Development Efficiency: An Empirical Analysis Using the Novel Epsilon‐Based Measure and Global Malmquist–Luenberger Index

ABSTRACTThe profound consequences of ecological degradation on humanity's well‐being are a severe matter acknowledged globally. This study examines the impact of ecological degradation on green development efficiency in less developed countries. A dataset from 1990 to 2020 was used to break down the impacts of ecological degradation on green development efficiency. Green development efficiency was calculated based on the epsilon‐based measure model, while the dynamic change of green development efficiency was explored with the Global Malmquist‐Luenberger Index model. The panel‐corrected standard errors (PCSE) and feasible generalised least squares (FGLS) models are conducted to test the influence of ecological degradation on green development efficiency. The results show that ecological degradation inhibits green development efficiency, whereas green FDI, urbanisation, and economic growth benefit the increase of green development efficiency. In addition, government intervention shows a negative correlation with green development efficiency. The results of the spatial Durbin model (SDM) of green development efficiency also demonstrate an overall strong negative spatial spillover effect on local and neighbouring regions, with more significant effects at levels of the surroundings.

Analysis of the Dynamic Changes and Driving Mechanism of Land Green Utilization Efficiency in the Context of Beijing–Tianjin–Hebei Synergistic Development

Studying the development of land green utilization efficiency and the factors that influence it in the Beijing–Tianjin–Hebei region can improve the distribution of land resources among regions and reinforce interregional integrated planning. By constructing a super-efficiency SBM model, calculating the Malmquist–Luenberger index, and constructing a Tobit model, this study explores the spatial features and temporal variations of land green use efficiency in the Beijing–Tianjin–Hebei region from 2010 to 2022. It also examines the mechanism that drives land green use efficiency in the context of the Beijing–Tianjin–Hebei synergistic development. According to this research, Beijing has consistently had the highest land green usage efficiency and a strong green development strength, whereas Baoding, Xingtai, Handan, and other cities in Hebei Province have lower land green utilization efficiency. According to the geographical dimension, the research area’s land green use efficiency exhibits a pattern of “high in the middle and low in the surroundings”, with Cangzhou, Langfang, and Tangshan standing out in terms of both industrial transformation and ecological building. Based on the results of the driving mechanism of land green use efficiency, it is evident that while the degree of urbanization and population concentration has a negative effect on land green use efficiency, the degree of economic development, industrial synergy, opening up to the outside world, environmental regulation, and ecological output all have positive and promoting associations with it. In summary, increasing the optimization of the economic and industrial structure, bolstering technological innovation and policy coordination, and attaining a harmonious coexistence of the economy and ecology are all essential steps in the process to increase the land green use efficiency in the research area when attempting to achieve the goal of sustainable development in the region.

Citrus Industry Agglomeration and Citrus Green Total Factor Productivity in China: An Empirical Analysis Utilizing a Dynamic Spatial Durbin Model

In the context of increasingly severe resource and environmental constraints, examining the impact of citrus industry agglomeration on the green total factor productivity (GTFP) of citrus is of great importance for the sustainable development of the citrus industry and is crucial for promoting the green, high-quality growth of China’s agricultural sector. In this study, the global Malmquist–Luenberger productivity index (GMLPI) model was used to measure the GTFP of mandarins and tangerines based on inter-provincial panel data from China’s major citrus-producing regions between 2007 and 2022. The dynamic spatial Durbin model was employed to empirically analyze the effects of citrus industry agglomeration on the GTFP of mandarins and tangerines, including the disaggregation of its spatial spillover effects. The results indicate that, in terms of temporal dynamics, the GTFP, technical progress index (GTC), and technical efficiency index (GEC) of mandarins and tangerines significantly fluctuated, especially during the period from 2007 to 2015. Regional disparities in GTFP and the GTC are more pronounced for mandarins than for tangerines, while the GEC shows greater regional disparities for tangerines than for mandarins. The intensification of citrus industry agglomeration has had a significant positive impact on the GTFP of mandarins and tangerines, both locally and in neighboring regions. The spatial correlation of the green total factor productivity of mandarins and tangerines fluctuated; mandarins showed significant spatial aggregation in some years, while tangerines showed significant spatial dispersion in several years. The local Moran scatterplot further reveals the significant negative spatial autocorrelation of mandarin and tangerine green total factor productivity from 2007 to 2022. The direct, indirect, and total effects of citrus industry agglomeration on the GTFP of mandarins and tangerines are significant and positive in both the short- and long-term, with short-term benefits exceeding long-term effects. Consequently, enhancing regional cooperation and exchange while advancing citrus industry agglomeration is essential for sustained productivity growth.

Analysis of green productivity in manufacturing based on different air pollution levels

China’s manufacturing industry needs to change from a high-emission model to one that is more environmentally friendly to reduce air pollution and achieve sustainable growth. Therefore, it is important to decrease manufacturing waste gas emissions by improving green productivity. This paper expands on the theories of green economic growth and sustainable development to present a framework for analysis of manufacturing green productivity at various levels of air pollution. It applies the Metafrontier Malmquist Luenberger index and SBM model to data from 30 Chinese manufacturing sectors from 2012 to 2022 to identify the obstacles to developing green productivity in the manufacturing sectors. The results show that: (1) From 2012 to 2022, the overall waste gas emission efficiency and GTFP of China’s manufacturing industry showed an increasing trend. (2) The manufacturing industry with light air pollution had the fastest growth in GTFP, followed by manufacturing with heavy air pollution, and manufacturing with moderate air pollution had the lowest. (3) The primary driver of GTFP growth is technological progress, while technical inefficiency has severely hindered GTFP growth. (4) There is a “Matthew effect” in the green productivity of China’s manufacturing industry. High waste gas emission efficiency often accompanies high levels of GTFP, and vice versa. Finally, this paper proposes policy suggestions to improve the green productivity of the manufacturing industry and reduce air pollution.

Environmental–economic total factor productivity of vegetable production in China from the life cycle perspective

Enhancing agricultural green total factor productivity (GTFP) is crucial for promoting sustainable economic development. However, the absence of systematic approaches to assess the environmental-economic productivity from a full life cycle perspective hinders sustainable agricultural development and constrains the transition to a green economy. This study introduced an innovative framework of life cycle GTFP (LCGTFP) through interdisciplinary collaboration. This framework integrated regionalized life cycle impact assessment (LCIA), multimedia LCIA-based life cycle costing analysis, and the epsilon-based measure combined with the global Malmquist–Luenberger approach. This integration allows for the inclusion of comprehensive environmental burdens into GTFP analysis. Empirical results from the production of 10 types of vegetables in China reveal a “win–win” trend in vegetable industry from an environmental–economic perspective. The LCGTFP reached a value of 1.036 (CV: 0.021) over the past decade, indicating a 3.6% increase in environmental–economic productivity. However, challenges (e.g., fertilizer overuse and regression on the technical frontier) persist in sustainable vegetable production. This study highlights the necessity of technical dissemination, agricultural education, high-quality crop varieties, and e-commerce. Furthermore, the development of the vegetable industry must incorporate environmental and economic conditions, because environmental–economic misallocation occurs in areas that are recognized as having geoclimatic–locational advantages.

Does Government Digital Transformation Drive High-Quality Urban Economic Development? Evidence from E-Government Platform Construction

Digitalization represents a pivotal global development trend and serves as a significant force propelling economic and social transformation. This manuscript uses the global Malmquist–Luenberger (GML) model to estimate green total factor productivity (GTFP) across 284 Chinese cities from 2003 to 2018, taking the pilot policy of “construction and application of e-government public platforms based on cloud computing” as an example to assess the impact of government digital transformation on the qualitative development of the economy by using a difference-in-differences model to explore the path of its role and driving mechanism. The results reveal that government digital transformation promotes the qualitative improvement of the city’s economic development, and its driving effect shows a marginal incremental law. Moreover, government digital transformation can contribute to the formation of a “latecomer advantage” in the lagging regions, which creates a “catch-up effect” on the regions with favorable development foundations, excellent geographical conditions, high urban ranking, and high education quality. Additionally, government digital transformation boosts economic and social development quality through both innovation spillover and structural optimization.

The Impact of Digital Economy on TFP of Industries: Empirical Analysis Based on the Extension of Schumpeterian Model to Complex Economic Systems

The digital economy (DE) is a new driver for enhancing total factor productivity (TFP). Using panel data from 30 provinces in China between 2011 and 2022, this study measures DE and TFP using the entropy-weighted TOPSIS method and the Global Malmquist–Luenberger method and further examines the impact of DE on the TFP of industries. The main findings are as follows: (1) DE can significantly improve TFP, though the extent of improvement varies. DE has the greatest impact on the TFP of the service industry, followed by the manufacturing industry, with the weakest effect on the agricultural industry. (2) The enhancement effect of DE on agriculture and the service industry is more pronounced in the central and western regions, while the improvement effect on manufacturing is more evident in the eastern region. (3) DE has facilitated the improvement of TFP in manufacturing industries such as textiles and special equipment manufacturing, as well as in service industries like wholesale and retail. However, it has not had a significant impact on the TFP of industries such as pharmaceutical manufacturing and real estate. This study has significant theoretical value and policy implications for China and other developing countries in exploring DE and achieving high-quality industrial development.

Regional Differences, Dynamic Evolution, and Convergence of Global Agricultural Energy Efficiency

Understanding the regional disparities, dynamic evolution, and convergence–divergence characteristics of global agricultural energy efficiency is crucial for enhancing agricultural energy efficiency, ensuring food security, and responding to global green development trends. This paper utilizes 2002–2021 panel data from 144 countries globally, employing the epsilon-based measure–global Malmquist–Luenberger (EBM-GML) model to estimate agricultural energy efficiency, considering unexpected output. The Dagum Gini coefficient, kernel density estimation, spatial Markov matrix, and spatial convergence model are employed to explain the spatial patterns and evolving trends of global and regional agricultural energy efficiency at three levels: regional disparities, dynamic evolution, and convergence. The results indicate significant spatial heterogeneity in global agricultural energy efficiency, with Europe exhibiting the highest efficiency, followed by Asia and the Americas, while Oceania and Africa demonstrate the lowest efficiency. Agricultural energy efficiency globally and in each region continues to improve, with increasing regional disparities, and difficulties in grade transitions in agricultural energy efficiency across regions. Each region exhibits β-convergence characteristics, but the convergence rates vary, and various factors influence growth rates of agricultural energy efficiency differently across regions. Therefore, countries should tailor their strategies based on local conditions, considering their own resource endowments and developmental stages, and strengthen international exchanges and cooperation.

Smart-City Policy in China: Opportunities for Innovation and Challenges to Sustainable Development

Urban development relies on the promotion of innovation, while sustainable development is an inevitable requirement for green urban development. As the primary carrier of innovation and sustainable development, cities have seen the construction of smart cities become a hotspot topic of public concern against the backdrop of rapid advancements in information technology. Based on the Chinese smart-city pilot policies, this paper selects data from 278 prefecture-level cities between 2007 and 2020, employing difference-in-difference (DID), epsilon-based measures and global Malmquist–Luenberger index (EBM-GLM), and the Spatial Durbin Model (SDM) to analyze the direct impact, spatial effects, and regional differences of smart-city construction on urban innovation capacity and sustainable development. The research results indicate the following: (1) the implementation of smart-city policies significantly enhances the urban innovation capacity (UCI), but its impact on green total-factor productivity (GTFP) is unstable and even insignificant; (2) the UCI and GTFP of smart cities have spillover effects, and the implementation of policies may inhibit the improvement of UCI and GTFP in neighboring cities; (3) the impact of smart-city construction varies across different regions, with a more significant promotion effect on the innovation capacity of economically developed cities.

Assessing energy efficiency, regional disparities in production technology, and factors influencing total factor energy productivity change in the agricultural sector of China

Efficiently utilizing the energy resources in the agriculture sector to produce more agricultural output with minimum environmental degradation is a shared global challenge. The Chinese government has introduced various policies aimed at enhancing energy efficiency (EE) and total factor energy productivity (TFEP) while addressing regional technological disparities in the agricultural sector. This study utilized DEA Super-SBM, Meta frontier Analysis, and the Malmquist–Luenberger index to assess energy efficiency, changes in total factor energy productivity, and the regional technology gap ratio (TGR) across 30 provinces in mainland China and three distinct regions during the period from 2000 to 2020. The findings reveal that the average EE in China's agricultural sector is 0.8492, indicating that, on average, there is a 15.08 % potential for improvement in EE growth within the sector. Qinghai (1.5828), Shanghai (1.3716), and Hainan (1.3582) are found to be the top 3 performers with the highest EE levels. The Eastern region demonstrates high excellence in EE, with a value of 1.0532. The TGR value of Zhejiang indicates the superior production technology utilized in the agriculture sector to utilize energy resources efficiently. Except for Zhejiang, the TGR of Liaoning, Jiangsu, Shanghai, Guangdong, Ningxia, and Hainan is above 0.96 and near 1, indicating superior production technology in the agriculture sector of China. The Technology Gap Ratio (TGR) of China's eastern region is superior to that of the central and western regions, consistently approaching 1. This suggests that the eastern provinces possess more advanced agricultural technologies, allowing them to optimize resource utilization for maximum output. The Malmquist–Luenberger index (MLI) score of 1.103 indicates a 10.3 % growth in the total factor energy productivity of China's agricultural sector. Further analysis reveals that this growth is primarily driven by technological change (TC), with a TC value of 1.080 surpassing the efficiency change (EC) value of 1.028. Among the three agricultural regions, the eastern region exhibits the highest total factor energy productivity. Specifically, Zhejiang (1.23), Shanghai (1.197), Liaoning (1.184), and Hebei (1.147) are identified as the top performers in total factor energy productivity growth in China's agricultural sector. Additionally, the Kruskal-Wallis test confirmed statistically significant differences in EE and TGR among the three regions.

Measurement of Tourism Ecological Efficiency and Analysis of Influencing Factors under the Background of Climate Change: A Case Study of Three Provinces in China’s Cryosphere

Against the backdrop of climate change and the “dual carbon” goals, enhancing the ecological efficiency of cryospheric tourism is crucial not only for the high-quality development of the tourism industry itself but also for the protection of the ecological environment and the promotion of green sustainable development in the cryospheric region. In light of this, this study, taking climate change as its background and based on the perspective of carbon emission constraints, integrates multidimensional factors such as “climate change, carbon emission constraints, and cryospheric resources” into a unified measurement framework to construct a model for evaluating the ecological efficiency of tourism in the cryosphere. Specifically, the model considers inputs, expected outputs, and unexpected outputs. Subsequently, employing the super-efficiency slack-based measure (SBM) model, this study measures the tourism ecological efficiency (TEE) of three provinces (Xinjiang, Qinghai, Tibet) in the cryosphere from 2013 to 2021 and utilizes the Malmquist–Luenberger index and gray correlation model to reveal their dynamic changes, efficiency decomposition, and influencing factors. The results indicate that: (1) The overall mean of TEE in the cryosphere is between 0.2428 and 1.2142, Over the study period, the average annual growth rate and corresponding confidence interval were 14.74%, (−8.61%, 64.23%), showing a significant fluctuating growth trend. Among them, Xinjiang stands out, with its mean scores ranging from 0.2418 to 1.6229, surpassing the overall average level of the cryosphere. (2) During the study period, the overall dynamic efficiency of tourism ecology in the cryosphere increased by 21.54%, driven by the synergy of technological progress (TC), pure technical efficiency (PET), and scale efficiency (SE). For each province, the dynamic efficiency of tourism ecology has improved, but to varying degrees. (3) Regarding the driving factors of TEE in the cryosphere, each driving factor is closely related to TEE, ranked from large to small as follows: carbon emission structure, level of economic development, infrastructure, intensity of technological input, industrial structure, resource endowment, and environmental regulation. This article holds theoretical and practical significance for promoting the high-quality development of polar tourism and achieving synergistic progress between the economy and environment.

Evaluation of regional comprehensive development efficiency under low-carbon policy: based on optimized DDF-GML combined with unsupervised clustering method

In the study of urban development, it is very important to evaluate the influence of production factors reasonably and efficiently for the region to achieve efficient development. The principal aim of this investigation is to amalgamate the conventional measurement model characterized by robust interpretability with the non-parametric model characterized by limited interpretability, thereby enhancing the precision of research outcomes. Towards this objective, the study employs an optimized directional distance function integrated with a global Malmquist–Luenberger index to formulate a comprehensive total factor productivity measurement framework. In elucidating the homogeneous attributes of regions, departing from prior methodologies reliant on manual or direct algorithmic partitioning, this paper employs the K-means clustering algorithm for index discernment, abstracting the concept of K-means clustering centroids to encapsulate regional homogeneity, thereby delineating results through the visualization of regional development potential maps and the evolution of centroid-based clustering trend maps. The findings of the investigation illuminate common patterns of change across disparate regions, proposing a strategy for leveraging regional resource endowments towards a cohesive framework, thereby transcending constraints imposed by production efficiency limitations. Amidst the backdrop of the COVID-19 pandemic, this study draws upon provincial-level data spanning from 2000 to 2018 in China. The conclusive analytical outcomes underscore the pivotal role of energy factors in regional development efficiency, particularly within high-potential development regions, followed by the capital and labor factors. Concurrently, the study discerns a discernible hierarchical pattern among areas of development potential, which exhibits correlation with factor mobility dynamics.

Countries’ green brands within the context of sustainable development goals

This study aims to fill the gap in estimating the Green Country Brand Strength Index and its convergence among EU countries and Ukraine (as a potential EU candidate) from 2006 to 2020. Drawing upon data from the World Data Bank, Eurostat, and The Food and Agriculture Organization, the study employs various analytical tools including principal component analysis, the global Malmquist‒Luenberger productivity index, the entropy method, and σ- and β-convergence theories. The findings suggest a notable increase in the green brand values of the analyzed countries from 2006 to 2020, with a significant expansion of the measurement scale. Notably, France, Germany, Spain, the Netherlands, and Sweden emerged as leaders in the Green Country Brand Strength Index by 2020. Furthermore, the study uncovers convergent policies among EU countries aimed at fostering green brands aligned with the Sustainable Development Goals, as evidenced by a decline in σ- and β-convergences. This research provides valuable insights into the dynamics of green branding and policy convergence within the EU and prospective candidate countries. It introduces a distinctive approach to assessing and analyzing green brand strength, emphasizing its critical importance to the formulation of sustainable development policies and strategies.

Impacts of Cropland Utilization Patterns on the Sustainable Use Efficiency of Cropland Based on the Human–Land Perspective

Confronted with China’s burgeoning population and finite arable land resources, the enhancement of sustainable arable land efficiency is of paramount importance. This study, grounded in the United Nations Sustainable Development Goals (SDGs), introduces a robust framework for assessing sustainable arable land use. Utilizing the Sustainable Utilization of Arable Land (SUA) indicator system, the DGA–Super-SBM model, the Malmquist–Luenberger production index, and the TO–Fisher–OSM algorithm, we evaluated the efficiency of sustainable utilization of arable land (ESUA) in 52 prefecture-level cities within China’s major grain-producing regions of the Yellow and Huaihai Seas. We analyzed the cropland utilization patterns from 2010 to 2020, examining the influence of these patterns on sustainable utilization efficiency. Our findings indicate that between 2010 and 2020, the arable land usage in these regions exhibited minimal transformation, primarily shifting towards construction land and conversely from grassland and water systems. Notably, the ESUA of arable land demonstrated an upward trend, characterized by pronounced spatial clustering, enduring high efficiency in the northern regions, and a significant surge in the southern sectors. The declining ESUA (D-ESUA) trend was general but increased in half of the cities. The change in the center of gravity of ESUA correlated with the north–south movement of the proportion of cultivated land area, the turn-in rate, and the turn-out rate, yet moved in the opposite direction to that of cultivated land density and yield per unit area. Variables such as the replanting index, cropland density, yield per unit area, and cropland turn-in rate significantly affected ESUA. These findings offer a scientific basis and decision-making support for optimizing the utilization pattern of arable land and achieving a rational allocation of arable land resources.

Provincial Coal Flow Efficiency of China Quantified by Three-Stage Data-Envelopment Analysis

The exploration of regional variations in coal flow efficiency (CFE) in China and the collaborative strategies for emission reduction are vital for accelerating the progress of ecological civilization within the coal industry and achieving an optimal allocation of coal resources. To unveil the evolutionary traits of actual CFE and its decomposition, this study employs a current technology based on a combined super-efficient measure (SBM), global SBM, the stochastic frontier approach (SFA), and the global Malmquist–Luenberger index (GML) model on panel data from 2010 to 2021 across 30 provinces in China. The research conclusions are as follows. First, significant efficiency gaps are observed among provinces, showcasing superior performance in the north and east regions. Moreover, the impact of environmental factors and random disruptions on individual slack variables varies, resulting in a decrease of 0.18 and 0.43 in the CFE of source-area and sink-area when these factors are not taken into account. Thirdly, a clear distinction emerges between the technical efficiency change index (EC) and the best-practice gap change index (BPC). Lastly, the CFE displays regional disparities marked by an upward trajectory and fluctuating patterns resembling a “W” shape.

Evaluation of China’s Marine Aquaculture Sector’s Green Development Level Using the Super-Efficiency Slacks-Based Measure and Global Malmquist–Luenberger Index Models

Given China’s rapidly expanding marine aquaculture industry, the associated ecological issues have garnered widespread attention. Therefore, it is crucial to speed up the green growth of marine aquaculture in order to save the environment and use resources sustainably. In order to statically assess and dynamically analyze the green development efficiency levels of marine aquaculture in nine coastal provinces of China from 2012 to 2021, this study uses the non-expected output super-efficiency Slacks-Based Measure model and the Global Malmquist–Luenberger index method. Additionally, it integrates input–output redundancy rates to analyze the causes of efficiency loss. Static efficiency primarily reflects whether a region’s inputs and outputs at a given point in time reach an effective efficiency level, while the level of dynamic efficiency mainly gauges the dynamic changes in the efficiency of green production. The results show that, from 2012 to 2021, China’s marine aquaculture industry’s average static efficiency of green output was 0.705. The southern marine economic zone exhibited the highest static efficiency value in the green development of marine aquaculture, displaying a stepped distribution pattern of “south–north–east” in decreasing order. The input–output redundancy analysis reveals that the primary causes of static efficiency loss in China’s marine aquaculture industry are attributed to varying degrees of redundant inputs and carbon emission outputs. Looking through the lens of the GML index, the annual average growth rate of the green total factor productivity in China’s marine aquaculture stands at 11.1%, with an annual average change in technical efficiency of 1.8%, while the annual average change in technological progress amounts to 9.1%, suggesting that technological advancement is the primary driver of the rise in green total factor productivity in China’s marine aquaculture sector. According to the study, in order to encourage China’s marine aquaculture industry to grow sustainably, efforts should be made not only to accelerate technological advancements but also to enhance technical efficiency. Policies that are specifically designed for the local environment should be developed to support the sustainable development of the marine aquaculture sector and to make resource allocation easier.

Green Energy Pathways Towards Carbon Neutrality

Trying to reach carbon neutrality is by no means plain sailing in times of energy crisis, price volatility, and war. The European Green Deal (EGD) prioritizes green pathways, but it is not enough when it copes with greenhouse gases (GHGs). The present research utilizes the Malmquist–Luenberger productivity index (MLPI) to estimate advancements in total factor productivity (TFP) in the European Union (EU). The study uses panel data from 1995 to 2019, in addition, there is comparison between two periods: 1995 – 1996 and 2018 – 2019, would provide important information about TFP progress or recession during a turbulent European era. Two MLPI models are applied, one that utilizes only non-renewable energy sources (NRES), while the other adopts renewable energy sources (RES). Encompassing inputs such as: electricity generation, labour force, and gross fixed capital formation (GFCF); desirable output: gross domestic product; and undesirable outputs: carbon dioxide (CO2) and methane (CH4). There is average productivity progress, more specifically the MLPI average productivity for NRES and RES is 2.14% and 7.34% respectively, meaning that the RES adoption leads to greater productivity performance by almost three times. This novel analysis might offer useful and practical information to policymakers through the measuring of TFP in order to effectively attain and accomplish carbon neutrality objectives.

Analysis of the impact factors of industrial structure upgrading on green total factor productivity from the perspective of spatial spillover effects

Green development has already been a vital part of China's high-quality economic progress in the future, and accelerating the restructuring and improvement of the industrial structure plays a crucial roll in promoting a regional green economy. In this article, based on the data of 278 prefecture-level cities in China from 2011 to 2020, the SBM directional distance function and Malmquist-Luenberger (ML) index are used to gauge regional green total factor productivity (GTFP). The spatial Durbin model is also introduced to investigate the spatial spillover mechanism of GTFP and its influencing factors under the optimized and upgraded industrial structure. The study indicates that the optimization of the industrial structure have an important effect in promoting the growth of GTFP. Specifically, industrial structure integration has a greater bearing on the increase in GTFP, while there is a difference in the effectiveness of industrial structural advancement and rationalization of GTFP. Economic growth level inhibits the enhancement of GTFP, and urbanization level and industrial agglomeration have a significant negative impact on the enhancement of GTFP in the spatial dimension. On a different hand, foreign investment degree and government intervention level have a significant positive affect on regional GTFP. In addition, this study fills the research gap of the regional industrial structure upgrading influence on GTFP, which has great theory and practice value for promoting China's high quality growth of green economy.

The heterogeneous impacts of environmental technologies and research and development spending on green growth in emerging economies: the moderating role of financial globalization

Introduction: Understanding the heterogeneous impacts of environmental technologies (ETs), research and development (RD) spending and financial globalization (FG) on green economic growth (GEG) is worthwhile to promote progress toward GEG. Besides, exploring the moderating role of FG is essential to uncover the nuanced dynamics that shape the relationship between ET, RD, GEG, and the influence of global financial integration. Thus, this study examines the effects of ET, RD and FD on GEG in emerging market economies (EMEs). In addition, we investigate the moderating role of FG on the effects of ETs and RD on GEG.Methods: The method of moments quantile regression (MMQR) is applied using a fixed effects model that can capture distributional heterogeneity and nonnormality concerns for the panel of 25 EMEs from 2000 to 2019. In addition, other alternative models are applied to conduct robustness analysis. We use green total factor productivity (GP) to proxy for GEG using the Malmquist–Luenberger Productivity Index (MLPI) strategy based on the directional distance function (DDF).Results and discussion: The findings imply that ETs significantly impact GEG, revealing evidence that promoting environmental innovation positively contributes to GEG progress in EMEs. Likewise, RD promotes GEG progression in EMEs. Additionally, FG positively impacts GEG. FG also positively moderates the effects of ETs and RD on GEG, implying that countries open to FG can better harness the positive roles of investment in ETs and RD on GEG in EMEs. Therefore, policymakers should develop prudent policies to encourage ETs and RD to promote GEG in EMEs, which aligns with the goals of controlling climate variation (SDG-13) and fostering innovation (SDG-9) to promote GEG in EMEs.

Study on the impact of green finance on green total factor productivity in forestry—evidence from China

China's forestry industry's ecological, economic, and social benefits have improved significantly. However, practical problems remain, such as irrational industrial structure, serious resource consumption, and low environmental transformation efficiency. The green total factor productivity is an important index to measure the transformation of economic development mode, and improving the green total factor productivity in forestry is of great significance in guiding the transformation of forestry economy, improving the structure of agricultural economy, and realizing the green development of forestry economy. Based on the panel data of 30 provinces and cities in China from 2006 to 2021, this article adopts the entropy value method and the Global Malmquist–Luenberger index based on the SBM directional distance function to construct the index system of green finance and forestry green total factor productivity and empirically analyzes the impact and mechanism of green finance on forestry green total factor productivity. It was found that 1) green finance can improve local forestry green total factor productivity and influence the neighboring regions to improve forestry green total factor productivity through economic spillover effect, and the coefficient of spatial spillover effect was 0.1041. 2) There were regional differences in the impact of green finance on forestry green total factor productivity, which was explicitly presented as a gradual decrease from east to west “ladder-like”. 3) There was a 30.46% intermediary effect of forestry industry structure in the influence of green finance on forestry green total factor productivity. 4) There was a double-threshold effect of human capital in the influence of green finance on forestry green total factor productivity, and there was a non-linear effect of marginal increment in the influence of green finance on forestry green total factor productivity after the threshold of 5.87 human capital. Based on China's ecological and economic development background, it was significant to utilize green finance to solve forestry economic development problems and promote China's green and sustainable development, and therefore put forward policy recommendations.