Developing a Conceptual Partner Matching Framework for Digital Green Innovation of Agricultural High-End Equipment Manufacturing System Toward Agriculture 5.0: A Novel Niche Field Model Combined With Fuzzy VIKOR

Digital governance and carbon emission reduction: Evidence from "National Pilot Policy of Information Benefiting the People" in China

Promoting digital governance is crucial for the Chinese government's governance reform. This study utilizes panel data covering 280 cities in China to examine the influence of government digital governance on reducing carbon emissions. The "National Pilot Policy of Information Benefiting the People," implemented in 2014, serves as the quasi-natural experiment for the analysis. The study employs the difference-in-difference (DID) methodology to indicate that improving the government's digital capacity contributes to reducing carbon emissions, which is robust to several robustness tests. Government digital governance facilitates the reduction of carbon emissions primarily via three mechanisms: developing green finance, gathering green talents, and promoting green technology innovation. Enhanced digital governance by the government exerts a more pronounced effect on reducing carbon emissions in cities in the eastern region, smaller municipalities, and resource-dependent municipalities. Additionally, current policies have notably decreased carbon emissions in the pilot cities, though a lag exists in the policy spillover effect affecting neighboring cities. This study investigates the influence of government digital governance on reducing carbon emissions through the lens of digital transformation. It offers valuable empirical insights for enhancing the governance capabilities of the Chinese government in the new era and facilitating the achievement of carbon reduction targets in urban areas.

Study on the impact of digital economy on industrial collaborative agglomeration: Evidence from manufacturing and productive service industries

In the digital era, digital economy has a far-reaching impact on the collaborative agglomeration of manufacturing and service industries. This research aims to examine the economic relationship between digital economy and industrial collaborative agglomeration. Based on a panel data set of 286 Chinese cities, this research employs Tobit model, moderating effect model, and mediating effect model to conduct data analysis. It is found that digital economy has a nonlinear relationship with industrial collaborative agglomeration, and this relationship is a U-shape. Moderating effect analysis reveals that government intervention significantly regulates the role of digital economy in industrial collaborative agglomeration. Mediating effect analysis indicates that digital economy promotes industrial collaborative agglomeration through entrepreneurial activity. Heterogeneity analysis shows that the facilitating effect of digital economy on collaborative agglomeration in high-end industries comes earlier than in middle- and low-end industries. Moreover, this research finds that digital economy plays a significant role in industrial collaborative agglomeration in central and western regions of China but not in the eastern region. To enhance the impact of digital economy on industrial collaborative agglomeration, it is crucial to strengthen the engagement of the government and ensure the availability of digital technology.

Coping with drug shortages: A study of government-enterprise option cooperation stockpiling strategies for drugs in shortage considering API surrogate stockpiling subsidies

Drug shortage is a global problem, and the development of government-enterprise cooperative stockpiles of drugs in shortage, combining physical and production capacity, has become one of the most important means of coping with drug shortages. However, existing studies have tended to overlook the fact that shortages of Active Pharmaceutical Ingredients (APIs) have become an important constraint on production capacity stockpiling and that the lack of incentives and provisions for coordination of benefits have led to a double marginal effect of joint stockpiling by government and enterprises of drugs in shortage. Accordingly, this study introduced the option contract to the drug supply system composed of government and pharmaceutical enterprises and used the subsidy of API storage in lieu as an important initiative to incentivize the reserve of APIs, to construct a model of shortage drug reserve under the government's leadership. This study aims to improve the effect of government-enterprise joint stockpiling of drugs in shortage, which is of great theoretical and practical significance. According to the classification of production license types of pharmaceutical enterprises, this study established a three-level supply chain decentralized decision-making model consisting of the government, formulation enterprises, and API enterprises, and a two-level supply chain centralized decision-making model consisting of the government and API Formulation (API-F) integrated enterprises, respectively. By solving the inverse order derivation, the government-enterprise option cooperation conditions and optimal decision-making strategy were derived. The study results showed that: (i) The addition of enterprise API stockpiling mode can help the government conventional reserves, and enterprise production capacity reserves, broaden the way of drug reserves, and improve the effect of government-enterprise option cooperation; (ii) when the probability of drug shortages is high, the government should prefer the cooperation of API-F integrated enterprises, which is conducive to reducing intermediate links and government costs and improving the supply responsiveness to shortages of medicines; (iii) Setting appropriate government subsidies for API storage can incentivize enterprises to stockpile APIs and improve drug production capacity and physical supply response capability. This study took the problem of socialized stockpiling of drugs in shortage as an entry point and explored the problems and solution strategies in the government-enterprise cooperative stockpiling of drugs in shortage, which not only made some theoretical contributions to the application of options contract in the government-enterprise cooperative stockpiling of drugs in shortage but also provided new ideas and theoretical basis for the improvement of the stockpiling work of drugs in shortage.

Digital economy and urban economic resilience: The mediating role of technological innovation and entrepreneurial vitality

Drawing on the diffusion of innovation theory, we argue that the development of digital economy has a positive effect on urban economic resilience. Using panel data from 284 cities in China from 2011 to 2018, we empirically examine the relationship between digital economy and urban economic resilience. We find a positive and significant link between them, mediated by technological innovation and entrepreneurial vitality. Moreover, the heterogeneity analysis shows that the impact of digital economy is most pronounced in smaller cities, with its effects diminishing in larger cities and megacities. Our results underscore the importance and the direction of fostering digital economy development.

Smart transition pathways and development incentive mechanism of China's smart community elderly care industry under market dominance: Considering a multi-subjective behavior game

Against the backdrop of an aging population, China is actively experimenting with an innovative elderly care model, so smart community elderly care has recently received widespread attention. However, the results of the implementation of the model have not yet met the expectation due to the variety of interests among the relevant participants. In this study, we identified the most core stakeholders in smart community elderly care, developed a four-party evolutionary game model including local governments, communities, service supply enterprises and households with elderly members. By applying the system dynamics method, we simulate the evolutionary paths and explore the complex interactions at the multiparticipant level in order to facilitate the transition of community elderly care services from traditional to smart, and then propose managerial insights for accelerating the construction of smart community elderly care. The results suggest that: (1) the four players in the game influence each other and are intimately related, and the benign interaction between them will further stimulate the vitality of the smart community elderly care industry; (2) appropriate improvement in policy support will strongly promote smart community elderly care, and the incentive effect on the demand side (households with elderly members) is more significant; (3) when households' preference for smart services increases, and the perceived value to communities and enterprises reaches a certain threshold, communities and enterprises will actively adopt smart solution and technology as well as develop stable portfolio strategy; (4) measures such as simultaneously increasing the level of smart and resource synergy will promote the system evolution toward smart services, and the system is more sensitive to the internal behavior of the enterprise than the external behavior between community and enterprise.

Developing a conceptual partner selection framework for matching public-private partnerships of rural energy internet project using an integrated fuzzy AHP approach for rural revitalization in China

The development of rural clean energy is the key to cope with the shortage of traditional energy supply in the rural revitalization strategy and improve the sustainability of rural energy supply. Under the background of digital age, the development and utilization of rural clean energy Internet has become the focus of rural economic development. The government partners of the Rural clean Energy Internet PPP project (RCEIPPPP) are the key to promoting the green and intelligent development of rural energy. In this paper, the index system of project partner selection is constructed, and the problem of government partner selection for RCEIPPPP is studied by AHP and fuzzy comprehensive evaluation. The results of this study are as follows: 1) Partners' financial ability, technical ability, management ability, performance experience, corporate reputation, cooperation ability and risk management are the influencing factors for government partner selection of rural clean energy Internet PPP projects (RCEIPPPPs); 2) Compared with other factors, financial ability, technical ability, management ability and performance experience are the four key factors that are more important in choosing partners; 3) The empirical research shows that AHP, fuzzy comprehensive evaluation and the index system constructed by this research can be applied to the practice of government partner selection for RCEIPPPPs. This study puts forward the evaluation system of government cooperation selection of energy Internet PPP projects from the theoretical level, improves the existing research methods, and makes the theoretical system in this field more complete. From the practical level, it provides scientific basis and suggestions for the government to make decisions on energy Internet PPP projects, and improves the engineering efficiency and quality of rural clean energy Internet construction. This study demonstrates the complexity of clean energy projects, the need for an integrated approach to decision-making, and the need for project managers to actively manage communication and collaboration with partners to ensure successful project implementation.

The synergistic evolution of supply-demand composite system for airport green development: A case study in Guangzhou Baiyun International Airport, China

Given the pressing requirements for sustainable development in civil aviation, conducting a synergistic evolution analysis of the supply and demand aspects in the airport green development holds great significance. This analysis helps achieve sustainable airport development and facilitates the green transformation of civil aviation development. Taking a collaborative learning approach and utilizing historical data from Guangzhou Baiyun International Airport spanning 2008 to 2019, the supply-demand composite system for airport green development was deconstructed into two subsystems-demand and supply-and relevant evaluation index systems were established in this paper. A screening and optimization model of supply and demand synergy indicators for airport green development was constructed, and it was solved using a simulated annealing genetic algorithm. The Haken model was constructed to analyze the synergistic evolutionary relationship of the composite system of supply and demand for green airport development in two stages. The results indicate a shift in the order parameter of the co-evolution of the supply-demand composite system at Guangzhou Baiyun International Airport, moving from the demand subsystem in the first stage (2008-2015) to the supply subsystem in the second stage (2016-2019). The co-evolution of the airport supply-demand composite system has entered a new stage, but has not reached a high level of synergy. The study not only contributes theoretically by explaining the interaction mechanism between supply and demand for airport green development, but also offers targeted suggestions for achieving high-quality synergistic evolution of supply and demand for airport green development.

The impact of the energy-consuming right trading system on corporate environmental performance: Based on empirical evidence from panel data of industrial enterprises listed in pilot regions

The energy-consuming right trading system (ECRTS) is a significant institutional innovation in China to address the increasingly severe energy crisis and environmental issues. Identifying the policy effects of energy consumption rights on corporate environmental performance (CEP) is conducive to achieving a win-win situation for China's economic growth and carbon neutrality. This study aims to analyze the impact of energy-consuming right trading system on corporate environmental performance and provide empirical evidence and policy implications for the full implementation of future policies. Using data from Chinese listed industrial enterprises from 2012 to 2019 and adopting the difference-in-differences method and mediation analysis, we empirically analyze the policy effects of energy-consuming right trading system. We find that the energy-consuming right trading system significantly promotes the improvement of corporate environmental performance, and the conclusion remains valid after a series of robustness tests. Further mechanism examinations indicate that the system mainly enhances environmental performance by affecting corporate green technological innovation. Heterogeneity tests suggest that the energy-consuming right trading system has a stronger impact on companies in economically developed regions, non-state-owned enterprises, and those with higher asset flexibility. Our research results can aid in the green transformation of enterprises and provide practical evidence for China to accelerate the comprehensive construction of the energy consumption rights trading market.

The impact of technological innovation on the green digital economy and development strategies

To investigate the interplay among technological innovation, industrial structure, production methodologies, economic growth, and environmental consequences within the paradigm of a green economy and to put forth strategies for sustainable development, this study scrutinizes the limitations inherent in conventional deep learning networks. Firstly, this study analyzes the limitations and optimization strategies of multi-layer perceptron (MLP) networks under the background of the green economy. Secondly, the MLP network model is optimized, and the dynamic analysis of the impact of technological innovation on the digital economy is discussed. Finally, the effectiveness of the optimization model is verified by experiments. Moreover, a sustainable development strategy based on dynamic analysis is also proposed. The experimental results reveal that, in comparison to traditional Linear Regression (LR), Decision Tree (DT), Random Forest (RF), Support Vector Machine (SVM), and Naive Bayes (NB) models, the optimized model in this study demonstrates improved performance across various metrics. With a sample size of 500, the optimized model achieves a prediction accuracy of 97.2% for forecasting future trends, representing an average increase of 14.6%. Precision reaches 95.4%, reflecting an average enhancement of 19.2%, while sensitivity attains 84.1%, with an average improvement of 11.8%. The mean absolute error is only 1.16, exhibiting a 1.4 reduction compared to traditional models and confirming the effectiveness of the optimized model in prediction. In the examination of changes in industrial structure using 2020 data to forecast the output value of traditional and green industries in 2030, it is observed that the output value of traditional industries is anticipated to decrease, with an average decline of 11.4 billion yuan. Conversely, propelled by the development of the digital economy, the output value of green industries is expected to increase, with an average growth of 23.4 billion yuan. This shift in industrial structure aligns with the principles and trends of the green economy, further promoting sustainable development. In the study of innovative production methods, the green industry has achieved an increase in output and significantly enhanced production efficiency, showing an average growth of 2.135 million tons compared to the average in 2020. Consequently, this study highlights the dynamic impact of technological innovation on the digital economy and its crucial role within the context of a green economy. It holds certain reference significance for research on the dynamic effects of the digital economy under technological innovation.

Artificial intelligence and green product innovation: Moderating effect of organizational capital

Green product innovation (GPDI) is crucial for addressing ecological issues and essential for enterprises' green operations and long-term growth. Digitization offers new possibilities for enhancing corporate green practices. Nevertheless, previous studies have predominantly addressed the association between overall digitalization and corporate green innovation, and research on the outcome of specific digital technology categories on green innovation is lacking. Within this framework, this study broadens the investigation into the connection between distinct categories of digital technologies and corporate green innovation. The period 2013-2022 was selected as the sample observation period, with companies listed on China's A-share market as the study objects. The fixed-effects model was applied to investigate the impact of artificial intelligence (AI) on firms' GPDI while exploring the interaction effect of firms' organizational capital. The findings indicate that AI is beneficial to GPDI in businesses. This effect is enhanced by employee and board human capital but diminished by board social capital. These results remained valid after two-stage least squares regression. This study broadens the utilization of the resource-based view and dynamic capacity theory in business implementation. Furthermore, it extends the resulting study of AI and provides a digital enhancement pathway for corporate GPDI. This study has significant theoretical and practical implications.

Multi-group symbiotic evolutionary mechanisms of a digital innovation ecosystem: Numerical simulation and case study

In the digital innovation ecosystem, the symbiosis mode formed between ecosystem members not only relates to their survival and development but also affects the ecosystem's symbiosis evolution mechanism. Based on symbiosis theory, this study first explores the evolutionary equilibrium strategy and its stability for three types of populations-core enterprises, digital platforms, and university research institutes-and then uses numerical simulation and a case study to explore the symbiotic evolution mechanism of the digital innovation ecosystem. The results show that: First, the digital innovation ecosystem is a complex adaptive system in which the three types of populations form different symbiotic relationships under different symbiotic modes and conduct symbiotic activities, such as value co-creation, to characterize the unique symbiotic evolutionary structure. Second, in this ecosystem, the symbiotic relationship formed by the combined values of different symbiotic coefficients between populations determines the outcome of symbiotic evolution. Third, the ideal direction of the evolution of the digital innovation ecosystem is a mutually beneficial symbiotic relationship. Thus, the symbiotic relationship between populations should be transformed into a mutually beneficial symbiotic relationships as much as possible. This study makes theoretical contributions by shedding light on the symbiotic evolution mechanism of the digital innovation ecosystem. It also offers countermeasures for the digital innovation cooperation of various stakeholders in China's digital innovation ecosystem.

What drives the green development behavior of local governments? A perspective of grounded theory

Although the elements that lead local governments to adopt sustainable development behaviors have been examined, the underlying processes that local governments adopt to accomplish green development behavior (GDB) lack systematic theoretical analysis. This study aims to investigate the determinants influencing local governments' implementation of GDB from the organizational internal and external perspectives. This study employed grounded theory to analyze the data and develop an influencing factor model of local government green development behavior (GDB-LG) after interviewing 53 Chinese local officials. Additionally, through integrating process organization research with new institutional theory, the mechanism that explains how these elements influence GDB was investigated. The results of the study demonstrate that the influencing factors model could give municipal governments clear guidance when creating sensible green development policies, further enhancing the efficacy of GDB.

Incentive mechanism of multiple green innovation behaviors of equipment manufacturing enterprises: A managers, green coordination groups and employees perspective

Employees play a pivotal role in the implementing of green development strategies and the attainment of dual-carbon objectives within manufacturing enterprises. Effective motivation of employees, fostering consensus on environmental protection, increased engagement in environmental initiatives, and the cultivation of employee cohesion are all vital for fostering green development within these enterprises. This paper seeks to elucidate the roles of general managers, green coordination groups (GCG), and employees in actualizing green behaviors. Furthermore, it advocates for a double incentive model to be employed in the implementing of green strategies within manufacturing enterprises. The research reveals that multiple factors, including incentive intensity, green capability, effort cost, risk aversion, and green variance, significantly influence the formulation of incentive contracts for green behaviors. The motivation level of the general manager directly impacts the efforts of the GCG, the organization's green climate, the manager's individual efforts, and indirectly influences the motivation and efforts of employees towards green behaviors. Notably, the influence of the organization's green climate on employees surpasses than on the manager, underscoring the imperative for collaboration efforts between the general manager and GCG to instill green behaviors among employees. Hence, it is imperative for the general manager and GCG to collaborate not only on critical aspects of green strategy implementation but also in fostering green behaviors among employees. This collaboration will facilitate the development of a multi-layer incentive mechanism aimed at promoting and facilitating the adoption of green behaviors among employees, thus contributing to the advancement of theory regarding employees' green behaviors and offering practical guidance for effectively realizing dual-carbon targets and achieving high-quality development within enterprises.

Agricultural land management and rural financial development: coupling and coordinated relationship and temporal-spatial disparities in China

The integrated development of agricultural land and finance not only promotes rural financial innovation and breaks the bottleneck of agricultural financing but also facilitates agricultural land transfer and scaled operations. This leads to the advancement of the effective growth of contemporary agriculture. The reform of the 'separation of three rights' in agricultural land promotes land circulation, which, in turn, offers an institutional guarantee for the tandem development of rural finance and agricultural land management. This paper measures the comprehensive development index of agricultural land management and rural finance in 30 provinces of China from 2005 to 2020. In light of this, it calculates the degree of coupling and coordination between China's agricultural land management and rural financial development. The Dagum Gini coefficient, kernel density, and the Moran index were used to analyze regional differences and patterns of agglomeration. The study found that the degree of coupling coordination between China's agricultural land management and rural finance is increasing annually. However, there remains a significant gap in achieving high-quality coupling. Notably, the growth rate of rural financial development exceeds that of agricultural land management, and hypervariable density is a major source of regional variation. There is polarization in the coupled development of farmland management and rural finance. Provinces in the eastern and central regions tend to be located in the high-high agglomeration (H-H) in terms of the level of development of agricultural land and financial integration, while the western region tends to fall in low-low aggregation (L-L).

The impact of market-incentive environmental regulation policies on corporate environmental costs: Evidence from China's carbon trading policy

As the world's largest emitter of carbon, China has implemented a series of environmental regulatory policies to reduce emissions. However, most of these environmental regulations have been at the expense of increased corporate environmental costs. Therefore, research on how to efficiently control these costs is of significant practical importance. This paper uses the China's carbon trading policy (CTP) implemented in 2013 as a quasi-natural experiment, utilizing data from Chinese listed manufacturing firms between 2008 and 2020. Employing a difference-in-differences (DID) model, the study investigates the impact of market-incentive environmental regulatory policies (ERP) on environmental costs. The findings reveal that CTP significantly reduced the environmental costs of firms, confirming the positive and vital role market-incentive ERP can play in environmental protection and cost control. These conclusions remain robust after a series of stability tests. Mechanism analysis suggests that the cost reductions brought by market-incentive ERP are primarily achieved through increasing green innovation. Heterogeneity analysis shows that non-state-owned enterprises (non-SOEs), key polluting firms, firms with lower financial constraints, and firms with lower total production efficiency benefit more from market-incentive environmental regulatory policies. This study provides new empirical evidence for government policy-making aimed at achieving long-term sustainable development.

Strategies selection for building e-commerce platforms for agricultural wholesale markets: A tripartite evolutionary game perspective

The rapid advancement and widespread implementation of digital technology have created opportunities for the e-commerce transformation of agricultural wholesale markets. The building of e-commerce platforms in this process is of utmost importance and should be approached methodically. This article analyzes the interests and behavioral choices of the agricultural wholesale markets, local government, and wholesalers by establishing a tripartite evolutionary game model. It applies replicator dynamics equations to describe the evolutionary strategies of each party. The findings of the study indicate that the behavioral choices of agricultural wholesale markets, local government, and wholesalers are influenced by their initial intentions. Furthermore, there exists a degree of alignment between the choices made by agricultural wholesale markets and wholesalers. The building of e-commerce platforms by agricultural wholesale markets can be facilitated through direct and indirect government subsidies; this also motivates wholesalers to adopt and utilize these platforms. Agricultural wholesale markets may further incentivize wholesalers to utilize their own e-commerce platforms by offering additional benefits. On the other hand, if the agricultural wholesale markets demonstrate strong initial inclinations toward using third-party e-commerce platforms. In this scenario, the local government has the potential to promote the widespread use of these platforms by providing both direct and indirect financial incentives to these markets, as well as actively encouraging wholesalers to participate in them. This study presents policy recommendations for agricultural wholesale markets and local government to support the effective implementation of e-commerce platforms in the agricultural wholesaler markets and facilitate a smooth transition to e-commerce in agricultural wholesale markets.

Digital economy drives regional industrial structure upgrading: Empirical evidence from China's comprehensive big data pilot zone policy

With the development of the digital economy, industrial structure upgrading plays an important role in realizing high-quality development. Exploiting the quasi-natural experimental setting provided by the Big Data Comprehensive Pilot Zone (BDCPZ) policy in China in 2016, this study evaluates the impacts of the BDCPZ policies on regional industrial structure upgrading using a combination of propensity score matching and difference-in-differences (PSM-DID) with panel data of 30 regions for the period 2008-2021. The results are as follows: (1) BDCPZ policies significantly promote regional industrial structure upgrading. This finding holds after conducting the placebo test and replacing explained variables. (2) BDCPZ policies enhance upgrading through technological innovation and financial deepening. (3) Heterogeneity analysis shows that the promotional effect of BDCPZ policies on industrial structure upgrading is more obvious in economically developed regions, megacities, and east-central regions; overall, regions with high industrialization benefit more. These findings have important implications: First, they provide new empirical evidence from the perspective of policy evaluation on how the digital economy affects industrial structure upgrading. Second, this study sheds light on the mechanism underlying this relationship, helping us understand how the digital economy can further affect the development of the industrial structure. Third, the policy effect is heterogenous, providing a scientific basis for the government to formulate differentiated implementation policies for different regions. This can help local industrial transformation and upgrading, and economic development in these regions through the implementation of big data and digital technologies.

A study of steady-state strategies for collaborative quality improvement in new retail service supply chains considering emotional factors

How to improve the quality of the new retail service supply chain (RSSC) has become a hot topic for enterprises and consumers. Considering the influence of the new RSSC enterprises' emotional attitude on the decision-making of quality improvement, the theory of rank-dependent expected utility (RDEU) is combined with an evolutionary game, constructing an evolutionary game model of collaborative quality improvement of new RSSC, and analyzing the game strategy choice of each participant in collaborative quality improvement of new RSSC. The study shows that when only one party is emotional, the rationality of retail service integrators will promote the synergistic improvement of the quality of the new RSSC more than functional service providers. Moreover, pessimism and optimism have an inverted U-shaped effect on quality decisions. When both parties have emotions, functional service providers remain optimistic, and retail service integrators remain pessimistic or rational can promote the collaborative improvement of new RSSC quality. In addition, the effects of quality preference, peer mechanism, feedback mechanism, and risk mechanism on the collaborative quality improvement of new RSSC are analyzed. Based on the research findings, relevant countermeasures are proposed to incentivize new retailers to conduct collaborative improvement in quality in terms of establishing an open mechanism for negotiation and consultation, strengthening the emotion management of new retail node firms, and adjusting the transmission of quality signals, with a view to realizing the quality collaborative improvement of the new RSSC.

Threshold effect of technological innovation on carbon emission intensity based on multi-source heterogeneous data

It is of much importance to clarify the impact of technological innovation on carbon emission intensity for the low-carbon transformation of China's economy. This study, based on the panel data of 30 Chinese provinces and municipalities from 2010 to 2020, measures and analyzes the carbon emission intensity and the level of technological innovation, establishing a spatial econometric model to study the spatial spillover effect and a panel threshold model to analyze the nonlinear influence of technological innovation level on carbon emission intensity. The findings are as follows: First, the overall carbon emission intensity in China shows a decreasing trend from 2010 to 2020, with the average dropping from 3.09 in 2010 to 1.98 in 2020; Second, the spatial autocorrelation results reveal that the level of technological innovation and carbon emission intensity in China are obviously aggregated in the global spatial distribution pattern. Third, the regression results of the spatial econometric model show that the direct effect of technological innovation on carbon emission intensity is significantly negative at the level of 1%, that is, the improvement of the technological innovation in a certain area has a significant inhibitory effect on carbon emission intensity. Fourth, based on the level of economic development, there is a significant three-threshold effect of the level of technological innovation on carbon emission intensity in China, and the influence of the level of technological innovation on carbon emission intensity varies in the direction of existence and coefficient values within different threshold intervals. As economic development reaches the third interval, the technological innovation level has the most significant inhibition on carbon emission intensity. These findings enriches the research of the nonlinear relationship between technological innovation and carbon emission intensity, clarifies the spatial spillover effect and threshold effect between among them, and provides inspiration for better promote the low-carbon transformation of economy.

Spatial-temporal coupling coordination and interaction between digitalization and traditional industrial upgrading: a case study of the Yellow River Basin

The realization of coupling coordination between digitalization and traditional industrial upgrading in the Yellow River Basin holds significant practical value for promoting high-quality industrial development in the region. In order to assess this coupling coordination, we utilized inter-provincial panel data from nine provinces in the Yellow River Basin, covering the period from 2011 to 2020. Through the application of a coupling coordination degree model, we calculated the degree of coupling coordination and relative development between digitalization and traditional industrial upgrading. Additionally, we conducted a spatial-temporal analysis to identify the characteristics and trends of digitalization and traditional industrial upgrading. Furthermore, we constructed a panel VAR model to examine the interactive relationship between these two factors. The findings are as follows: (1) overall, over the study period, the degree of coupling coordination between digitalization and traditional industrial upgrading in the Yellow River Basin transitioned from a disordered state to a run-in stage. The corresponding development type changed from a low steady state to a co-existence of low and medium steady states. Notably, the levels of digitalization, traditional industrial upgrading, and coupling coordination all exhibited a gradual increase, while the relative development degree declined. (2) The coupling coordination degree between digitalization and traditional industrial upgrading in the Yellow River Basin demonstrated significant regional variation. Provinces displaying a "high-high" agglomeration distribution and "low-low" agglomeration distribution were concentrated in the middle and lower reaches, as well as the upper reaches, of the Yellow River. Furthermore, there was a positive spatial autocorrelation between these regions. (3) Both digitalization and traditional industrial upgrading exhibit self-reinforcing mechanisms, and a long-term dynamic correlation exists between them.

Optimal operation mode selection of sponge city PPP projects using IFSS-prospect theory and VIKOR approach: a case study in China

The construction of sponge city (SC) public private partnership (PPP) projects is considered to be a feasible measure to solve the "water problems" in China. To ensure the success of SC PPP projects, how to select an appropriate operation mode for SC PPP projects is an urgent problem to be solved. In this paper, first, the evaluation index system of operation mode is established from the aspects of project characteristics, government capabilities, project benefits, project implementation, and cooperative partnership. Second, the DEMATEL method and entropy method are introduced to overcome the drawbacks of a single method, and the combination weights are calculated by game theory. Finally, the decision-making model based on intuitionistic fuzzy soft set (IFSS)-prospect theory and VIKOR approach is constructed to select operation mode for SC PPP projects. In this paper, the framework is applied to select an operation mode for the Qingshan demonstration zone project, and its feasibility and effectiveness are verified through sensitivity analysis and comparative analysis. The results show that BOT is the optimal operation mode for SC PPP projects, and government capabilities and project implementation are critical factors to be considered when selecting an operation mode. This study can provide guidance to the government in the OMS of SC PPP projects and improve the sustainability of the projects.

Insight into the Balancing Effect of a Digital Green Innovation (DGI) Network to Improve the Performance of DGI for Industry 5.0: Roles of Digital Empowerment and Green Organization Flexibility

Under the background of double carbon target and digital intelligence era, the innovation resources and innovation environment that manufacturing enterprises rely on have shown exponential growth. Digital green innovation (DGI) has gradually become the mainstream paradigm of innovation. How to achieve a balance between a local DGI network (LDGIN) and a remote DGI network (RDGIN) and how to use the role of digital empowerment and green organization flexibility to improve the performance of DGI are very important issues facing manufacturing enterprises at present. However, this problem has not been fully addressed in the existing research. In this study, the influence mechanism of LDGIN and RDGIN on the DGI performance of manufacturing enterprises was revealed, considering the moderating role of digital empowerment and green organization flexibility. The linear regression method was used to analyze the 562 valid data obtained by questionnaire survey. The results of this study are as follows. The effect of the DGI network on manufacturing enterprises’ DGI performance is heterogeneous because of LDGIN and RDGIN. The establishment of embedded links in a DGI network inevitably requires manufacturing enterprises to pay the corresponding costs. The over-embedding of manufacturing enterprises into RDGIN will have a negative impact on DGI performance. The balance between LDGIN and RDGIN has an important impact on manufacturing enterprises’ operation. The comprehensive balance and relative balance indexes constructed in this paper show that an appropriate balance can promote the improvement of the DGI performance of manufacturing enterprises. Digital transformation and organizational structure innovation are changing the business model of manufacturing enterprises and can regulate the relationship between the LDGIN and RDGIN and the DGI performance of manufacturing enterprises. The balance of DGI network embedding in practice shows the important role and enlightening significance of local and remote search in developing countries.