Green Development Efficiency Research Articles

Research on the spatio-temporal coupling relationship between agricultural green development efficiency and food security system in China

Food security and agricultural green development are fundamental issues concerning the survival of mankind. To realize the coordinated development of them is of great significance to promote the green transformation of agriculture and ensure national food security. However, few studies have analyzed the coupling relationship between agricultural green development and food security. Therefore, this study complements the research on the coupling relationship between them. First, we use the agricultural panel data of 31 provinces in China from 2010 to 2021 to measure the agricultural green development efficiency and food security level by Super-SBM model and entropy weight method. Then, the coupling relationship and spatiotemporal evolution of the two are analyzed by coupling coordination degree model. The results show that, first of all, China's agricultural green development efficiency and food security level have improved overall, but there are regional differences. Secondly, the degree of coupling and coordination between the two is significantly improved, and most provinces develop from the break-in stage to the coordination stage, and the regional differences are relatively reduced. Finally, Beijing, Shanghai and other developed areas are mostly of the lagging food security type, Liaoning, Shandong and other major grain producing areas have reached a high degree of coordination, while Tibet, Qinghai and other western regions are still in the break-in stage. According to the development situation of different regions, corresponding suggestions are put forward. For regions with lagging food security type, the government should give full play to the application of science and technology in agriculture and promote green and low-carbon planting technologies. For regions with low coupling and coordination degree, the government should improve support policies and build a collaborative operation system, adjust planting structure to improve land utilization rate and food security level. Finally, the government should work together to build agricultural industrial parks and give full play to the leading role of competitive provinces to achieve common development.

Green Development Efficiency in Cultural Industries: The Role of Digitalization in China’s Provincial Context

Green Development Efficiency in Cultural Industries: The Role of Digitalization in China’s Provincial Context

Study on urban green development efficiency of Jiangsu, Zhejiang and Fujian in China: a mixed network SBM approach

PurposeBy studying the green development efficiency (GDE) of 33 cities in the provinces of Jiangsu, Zhejiang, and Fujian in China, this study strives to conduct an analysis of the sustainable practices implemented in these developed regions, and derive valuable insights that can foster the promotion of green transformation.Design/methodology/approachFirst, the urban green development system (GDS) was decomposed into the economic benefit subsystem (EBS), social benefit subsystem (SBS), and pollution control subsystem (PCS). Then, a mixed network SBM model was proposed to evaluate the GDE during 20152020, with Moran’s I and Bootstrap truncated regression model subsequently applied to measure the spatial characteristics and driving factors of efficiency.FindingsSubsystem efficiency presents a distribution trend of PCS > EBS > SBS. There is a particular spatial aggregation effect in EBS efficiency, whereas SBS and PCS efficiencies have no significant spatial autocorrelation. Furthermore, urbanization level contributes significantly to the efficiency of all subsystems; industrial structure, energy consumption, and technological innovation play a crucial role in EBS and SBS; external openness is a pivotal factor in SBS; and environmental regulation has a significant effect on PCS.Originality/valueThis study further decomposes the black box of GDS into subsystems including the economy, society, and environment. Additionally, by employing a mixed network SBM model and Bootstrap truncated regression model to investigate efficiency and its driving factors from the subsystem perspective, it endeavors to derive more detailed research conclusions and policy implications.

Optimizing green supply chain circular economy in smart cities with integrated machine learning technology

This paper explores methodologies to enhance the integration of a green supply chain circular economy within smart cities by incorporating machine learning technology. To refine the precision and effectiveness of the prediction model, the gravitational algorithm is introduced to optimize parameter selection in the support vector machine model. A nationwide prediction model for green supply chain economic development efficiency is meticulously constructed by leveraging public economic, environmental, and demographic data. A comprehensive empirical analysis follows, revealing a noteworthy reduction in mean squared error and root mean squared error with increasing iterations, reaching a minimum of 0.007 and 0.103, respectively—figures that are the lowest among all considered machine learning models. Moreover, the mean absolute percentage error value is remarkably low at 0.0923. The data illustrate a gradual decline in average prediction error and standard deviation throughout the model optimization process, indicative of both model convergence and heightened prediction accuracy. These results underscore the significant potential of machine learning technology in optimizing supply chain and circular economy management. The paper provides valuable insights for decision-makers and researchers navigating the landscape of sustainable development.

Carbon emission allowances and green development efficiency

This study delves into the nexus between the efficiency of carbon emission allocation and the efficiency of green development within the context of innovative city construction. Leveraging data from 283 prefecture-level cities and corresponding provincial-level panel data spanning the period from 2005 to 2019 in mainland China, the research employs the zero-sum gain DEA model to compute provincial carbon emission allocation efficiency and allocate allowances. Furthermore, it utilizes the all-factor non-radial directional distance function and the SBM-DEA model to gauge the green development efficiency of prefecture-level cities. The double-difference method is applied to evaluate the impact of innovative city construction on regional green development efficiency. The results indicate that an imbalance between carbon emission allocation efficiency and emission quotas detrimentally affects urban green development. However, technological progress, factor accumulation, and the institutional environment associated with innovative city building mitigate this negative impact. Heterogeneity analysis reveals a differentiated effect of innovative city building on regional green development efficiency. This paper not only explores the concept of achieving an equitable distribution of carbon emission allowances but also examines how innovative city building influences regional green development. By establishing connections between urban policies, the equitable distribution of carbon emission allowances, and regional green development, it introduces a new perspective for promoting high-quality development at the regional level.

Transportation infrastructure upgrading and green development efficiency: Empirical analysis with double machine learning method

In order to deal with the environmental problems such as pollution emissions and climate change, sustainable development in the field of transportation has gradually become a hot topic to all sectors of society. In addition, promoting the green and low-carbon transformation of China's transportation is also an important issue in the new era. Thus, it is particularly important to correctly identify the green effect of high-speed rail. However, the traditional causal reasoning model faces several challenges such as ‘dimensional curse’ and multicollinearity. Based on the panel data of 283 prefecture-level cities in China from 2003 to 2019, this study uses the double machine learning model to explore the impact of transportation infrastructure upgrading on the efficiency of urban green development in China. The research shows that the upgrading of transportation infrastructure can effectively improve the efficiency of urban green development by 4%. Service industry agglomeration and green innovation are verified as two mediating channels. Moreover, the synthetic difference in difference model is employed to evaluate the regional impact of high-speed rail, and finds that the regional impact of transportation policies often exceeds the impact of individual cities. We further apply the conclusions of this paper to the research at the micro enterprise level. Goodman-Bacon decomposition and a variety of robustness tests confirm the validity of our conclusions. The study's comprehensive empirical analysis not only validates the positive effects of transportation upgrades on green development, but also offers novel insights into the underlying mechanisms and policy implications of transportation upgrading.

Local Government Debt, Local Government Financing Platforms, and Green Development Efficiency

Local government debt (LGD), an important financing tool for local governments to achieve high-quality development, especially through the expansion of urban investment bonds led by local investment and financing platforms, has a significant impact on green development efficiency (GDE). This article starts from LGD, selects data from 30 provinces from 2010 to 2019, and uses a two-way fixed-effect model and mediation-effect model to empirically analyze the impact of LGD on GDE. The research results show that LGD has a significant inhibitory effect on GDE, and LGD can indirectly affect GDE through the degree of industrialization and the number of the urban population. This article suggests that the government should make even fuller use of LGD funds, optimize the use of relevant funds, standardize the operation of local government investment and financing platforms, reduce debt risks, promote industrial structure upgrading, promote urban population resettlement measures, and improve China’s GDE.

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.

The Effect of Government Environmental Auditing on the Efficiency of Green Development in the Yangtze River Economic Belt under the Background of Big Data

The issue of environmental pollution has grown more noticeable due to the rapid expansion of our country's economy. The urgent demand for sustainable growth and the protection of ecological environment has arisen. In such a context, the role of governmental environmental audits is of utmost importance. This article is based on the background of the big data, from a variety of vantage points, this paper investigates the mechanisms and direct effects of government environmental assessments on green development efficacy in 108 localities along the Yangtze River Economic Belt (YREB). The study's results signify the following: (1) Government environmental evaluations substantially increase green development efficacy, even when no other control variables are taken into account. (2) The principal factors that impact such efficiency in the YREB are industrial structure upgrading and local government competition. (3) Robust tests conducted through variable substitution, estimation method replacement, and sample period alteration consistently yield significant results. This research holds significant implications for expanding the research perspective on governmental environmental audits, addressing ecological environmental issues, promoting high-quality green growth in the YREB, and achieving sustainable growth.

Spatial–temporal evolution and peer effects of urban green development efficiency in China

In the process of global urban development, there are urgent ecological security and environmental pollution problems, green development is the fundamental way for urban sustainable development, economic transformation and mitigation of ecological and environmental problems. Based on the panel data of 283 cities at prefecture level and above in China from 2003 to 2017, this paper analyzes spatial–temporal evolution characteristics of urban green development efficiency (UGDE) and the peer effects of UGDE between cities of different grades. It is found that during the study period, in terms of temporal evolution, the average UGDE in China increased from 0.47 in 2003 to 0.61 in 2017, with a cumulative growth rate of 29.79%, showing a rising trend in general. In terms of spatial evolution, the number of low-efficiency cities and medium-efficiency cities continued to decrease. The eastern region has always been the main distribution area of higher-efficiency cities and high-efficiency cities; in the central region, UGDE in most cities improved significantly; in the western region, UGDE has always lagged behind that in the eastern and central regions. In addition, the center of gravity of UGDE presented a trend of northwest migration in general, with a total displacement of 100.07 km, and UGDE showed a spatial dispersion trend. The empirical results indicate that the improvement of UGDE in large cities has a driving effect on that in neighboring medium cities and small cities through the positive peer effect, and the growth of UGDE in medium cities has a promoting effect on that in neighboring small cities through the positive peer effect; the increase of UGDE in medium cities has a positive peer effect on that in neighboring large cities, and the growth of UGDE in small cities has a positive peer effect on that in neighboring medium cities; UGDE promotes each other between large cities through the positive peer effect.

Efficiency of green and low-carbon coordinated development for mega urban agglomerations: an empirical study

As a critical engine for national economic growth, mega urban agglomerations have significant scale effects and economic and environmental spillover effects. This paper aims to study the green and low-carbon coordinated development of mega urban agglomerations to evaluate the country’s level of ecological civilization and its green and low-carbon development. The traditional research on green and low-carbon urban development tends to homogenize the redistribution theme, leading to significant errors in spatial allocation. This results in a lower accuracy of spatial distribution calculations for green development. Additionally, the research is constrained by data precision and methodology, making it challenging to measure the spatial differences in green and low-carbon development within urban clusters at the block level. This limitation hinders the ability to conduct detailed studies on the efficiency variations of green and low-carbon development in urban clusters. To achieve this aim, the study adopts the DPSR framework of the economic, resource, social, and ecological environment complex system and focuses on the Guangdong-Hong Kong-Macao Greater Bay Area in China. The study uses the entropy method, Gini coefficient method, and non-expected output super-efficiency SBM model to analyze the spatial effects and development efficiency of green and low-carbon development in this region from 2006 to 2020. The study results indicate that: (1) the overall level of green and low-carbon development in the Greater Bay Area is on the rise, with Shenzhen, Guangzhou, Foshan, and Zhuhai showing more stable development than other cities. Foreign direct investment and fixed asset investment in science and technology have significantly promoted green and low-carbon development. (2) The spatial differences in the region’s level of green and low-carbon development have narrowed trends, mainly due to differences between regions. However, well-developed cities such as Guangzhou and Shenzhen have taken the initiative to lead the development of other cities, fully leveraging their advantages in science and technology, geographical location, and other resources to promote the improvement of the external orientation of other cities. (3) The overall development efficiency of green and low-carbon in the Greater Bay Area is on the rise, with Guangzhou region showing overall stability, and Shenzhen region and Zhuhai region experiencing multiple ups and downs in their development. The three sub-regions show significant differences, but the balance and coordination of development have significantly improved. Finally, this study provides theoretical support for the future green and low-carbon development of urban clusters. It is advantageous for integrating the mainstream policy analysis framework of environmental economics with the complex adaptive systems of urban clusters. The research expands the boundaries of existing theoretical studies and offers new methodological approaches for interdisciplinary research. The study achieves a balance between the opportunity effects of green and low-carbon development and environmental policy constraints in super large urban clusters, effectively enhancing resource utilization efficiency in these clusters.

Research on Green and Low-Carbon Development Countermeasures of Anshan Iron and Steel Industry Under the Goal of "Double Carbon"

Green low-carbon and economic and social win-win is the only way to achieve sustainable development. Through the new growth point, new momentum and new path of green low-carbon development of Anshan iron and steel industry, Anshan City will continue to improve the quality and efficiency of green development. This paper mainly expounds the status quo and obstacles to the implementation of green and low-carbon development of Anshan iron and steel industry, and puts forward countermeasures and suggestions from the government, enterprises and the public to strengthen the construction of a new path to achieve green and low-carbon development of Anshan iron and steel industry.

Government environmental concern and urban green development efficiency: Structural and technological perspectives

Increased central government accountability regarding environmental issues is expected to prompt local governments to allocate more attention; however, will greater local government environmental concentration stimulate higher urban green productivity? We focus on answering this question and exploring the associated mechanisms. This study constructs a theoretical and analytical framework to examine the impact of government environmental concern (GEC) on urban green efficiency (UGE) and investigate the impact of GEC on UGE using a combination of fixed effects, threshold effects and spatial Durbin models. The findings reveal that GEC can significantly contribute to UGE and the conclusions remain robust following a series of tests. GEC can effectively promote UGE in eastern and low-pollution-emitting cities in contrast to mid-western and high-pollution-emitting cities. GEC also significantly contributes to industrial intelligence and green innovation, subsequently increasing UGE. Finally, we determine that GEC has a pollution halo effect in China. These findings provide empirical insights for local governments to effectively allocate resources to prioritised concerns.

How to improve the efficiency of green development? The role of digital finance

This paper investigates the relationship between digital finance and the green development efficiency based on 283 prefecture-level cities from 2011 to 2019. The research results show that: (1) The development of digital finance can significantly enhance regional green development efficiency. (2) Digital finance enhances green development efficiency through innovation effect and allocation effect. (3) Heterogeneity test show that digital finance has a more significant effect on green governance in the central region and regions with lower levels of digitalization.

How Does Livelihood Public Service Affect Urban Green Development Efficiency? Evidence from 281 Cities in China

How Does Livelihood Public Service Affect Urban Green Development Efficiency? Evidence from 281 Cities in China

The Impact of Digital Finance on the Green Utilization Efficiency of Urban Land: Evidence from 281 Cities in China

In the era of digital economy, digital finance, as an innovative financial model, plays an important role in driving urban industrial transformation and development, technological innovation, industrial upgrading and sustainable utilization of energy, and has a significant impact on sustainable urban development. At present, in the process of building a new pattern of Chinese-style modernization in China, it is of great significance to improve the green use efficiency of urban land through digital finance and realize the sustainable use of land resources and the sustainable development of the city. The current study employed 281 Chinese cities from 2010 to 2020 as research samples to investigate the effects of technological financing on the productivity of city land green usage. Based on the ideas of responsible growth and efficient urban development, an assessment index system was developed. Comprehensive empirical tests, such as the Super-SBM model, fixed effect model, and mediation effect model, were implemented in the research. The study’s findings indicate that: (1) Throughout the research period, the benchmark model’s regression outcomes demonstrate that digital banking impacts urban land’s green development efficiency, with positive moderating effects offered by environmental legislation; the optimization of industrial structure has not yet played a positive regulating effect. (2) Urban area green usage performance is more clearly impacted by the extent of use and the degree of digitization, according to the regression results of digital financing heterogeneity. The positive effect of online financial services on a city’s green use efficiency occurs mainly in eastern cities and southern cities, given the results of urban development level difference. In light of resource endowment unpredictability, “non-resource cities” stand to gain more from global finance’s encouragement of resource-efficient urban land use than do “resource cities”. The results of the mechanism test indicate that there is a strong mediating influence from digital finance, city land environmental use productivity, and green technological breakthroughs. In consideration of these results, the following measures are suggested in this paper: (1) Persist in advocating for the transformation of traditional finance into online financing. (2) Intensify the impact of significant variables on the environmentally friendly use of urban areas. (3) Encourage technology creativity and execution through the application of technological economics.

Digital technologies and corporate green innovation: opening the “black box” of resource orchestration mechanisms

PurposeDriven by the dual-carbon target and the widespread digital transformation, leveraging digital technology (DT) to facilitate sustainable, green and high-quality development in heavy-polluting industries has emerged as a pivotal and timely research focus. However, existing studies diverge in their perspectives on whether DT’s impact on green innovation is synergistic or leads to a crowding-out effect. In pursuit of optimizing the synergy between DT and green innovation, this paper aims to investigate the mechanisms that can be harnessed to render DT a more constructive force in advancing green innovation.Design/methodology/approachDrawing from the theoretical framework of resource orchestration, the authors offer a comprehensive elucidation of how DT intricately influences the green innovation efficiency of enterprises. Given the intricate interplay within the synergistic relationship between DT and green innovation, the authors use the fuzzy-set qualitative comparative analysis method to explore diverse configurations of antecedent conditions leading to optimal solutions. This approach transcends conventional linear thinking to provide a more nuanced understanding of the complex dynamics involved.FindingsThe findings reveal that antecedent configurations fostering high green innovation efficiency actually differ across various stages. First, there are three distinct configuration patterns that can enhance the green technology research and development (R&D) efficiency of enterprises, namely, digitally driven resource integration (RI), digitally driven resource synergy (RSy) and high resource orchestration capability. Then, the authors also identify three configuration patterns that can bolster the high green achievement transfer efficiency of enterprises, including a digitally optimized resource portfolio, digitally driven RSy and efficient RI. The findings not only contribute to advancing the resource orchestration theory in the digital ecosystem but also provide empirical evidence and practical insights to support the sustainable development of green innovation.Practical implicationsThe findings can offer valuable insights for enterprise managers, providing decision-making guidance on effectively harnessing the innovation-driven value of internal and external resources through resource restructuring, bundling and leveraging, whether with or without the support of DT.Social implicationsThe research findings contribute to heavy-polluting enterprises addressing the paradoxical tensions between digital transformation and resource constraints under environmental regulatory pressures. It aims to facilitate the simultaneous achievement of environmental and commercial success by enhancing their green innovation capabilities, ultimately leading to sustainability across profit and the environment.Originality/valueCompared with previous literature, this research introduces a distinctive theoretical perspective, the resource orchestration view, to shed light on the paradoxical relationship on resource-occupancy between DT application and green innovation. It unveils the “black box” of how digitalization impacts green innovation efficiency from a more dynamic resource-based perspective. While most studies regard green innovation activities as a whole, this study delves into the impact of digitalization on green innovation within the distinct realms of green technology R&D and green achievement transfer, taking into account a two-stage value chain perspective. Finally, in contrast to previous literature that predominantly analyzes influence mechanisms through linear impact, the authors use configuration analysis to intricately unravel the complex influences arising from various combinatorial relationships of digitalization and resource orchestration behaviors on green innovation efficiency.

How Does Artificial Intelligence Impact Green Development? Evidence from China

Artificial intelligence not only changes the production methods of traditional industries but also provides an important opportunity to decouple industrial development from environmental degradation and promote green economic growth. In order to further explore the green value of AI, this paper constructs an indicator of industrial robot penetration at the regional level, based on the idea of Bartik’s instrumental variable, and measures green development efficiency using the improved Super-SBM model. Based on a comprehensive explanation of the influence mechanism, a spatial measurement model and mediating effect model are constructed to test the spatial spillover effect and transmission mechanism between AI and green development. This study shows that (1) there is a significant inverted U shape in the impact of AI on green development; (2) the heterogeneity analysis finds that the structural dividend of AI is more obvious in capital-intensive and technology-intensive areas, which can more fully release its empowering effect on green development; (3) AI can not only directly affect green development but also indirectly affect green development by promoting green technology innovation and optimizing industrial structures, etc.; (4) AI has a significant inverted U-shaped spatial spillover effect on green development, and the development of local AI has a radiation-driven effect on the green development performance of its spatially related areas. The research methodology of this paper can be used for future research, and the results could provide support for the formulation of regional AI applications and green development policies.

How to achieve green development? A study on spatiotemporal differentiation and influence factors of green development efficiency in China.

For a long time, China 's extensive economic development model has produced a large amount of emissions, which has brought indelible damage to the environment. Green development is of vital importance for China to achieve high-quality development, and it is the core of alleviating environmental problems and promoting sustainable development. How to achieve China 's green development requires us to evaluate the level of green development in China 's provinces and analyze the reasons. In this study, an evaluation index system including undesired output of green development efficiency is constructed, and then the Supe-SBM model is used to assess the green development efficiency of 30 Chinese provinces. This paper also discusses the spatial and temporal differences as well as the factors affecting green development efficiency of green development efficiency among provinces. The findings demonstrate: (1) The green development efficiency in the eastern region is the highest, followed by the western region, while the central region has the lowest, but they all show a downward trend. (2) The spatial characteristics of green development efficiency are remarkable, according to the Global Moran's I index. However, the results of local spatial agglomeration demonstrate "small agglomeration and large dispersion," with the majority of provinces exhibiting L-L agglomeration. (3) Technological Progress, Opening Up, Urbanization Level are positively correlated with the green development efficiency. Industrial Structure, Financial Development, Energy Structure and green development efficiency are significantly negatively correlated, while Environmental Regulation shows no significant impact.

Environmental policies with the excess burden of public funds and privatisation

AbstractThis study investigates the interaction of environmental policies with the shadow cost of public funds and the efficiency of green research and development (R&D), while considering the presence of a public firm. In the policy combination of emission taxes and green R&D subsidies, we find that it has an implementable region where the shadow cost is neither high nor low, but the green R&D efficiency is high. We also demonstrate that the privatisation of public firms decreases the rates of both emission taxes and green R&D subsidies under a single policy regime. This increases (decreases) the implementable region of the policy combination of the emission tax (green subsidy) and is only implementable in the presence of a public firm. Finally, privatisation is shown to increase welfare when the shadow cost is lowest and green R&D is less efficient. These findings highlight that public ownership substitutes for environmental policies, while the shadow cost of public funds and the green R&D efficiency are critical when privatising public firms.