Efficiency In China Research Articles

Research on the Impact of Digital Finance on Energy Efficiency in China

Digital finance and green technology innovation (GTI) serve as powerful engines for promoting energy efficiency (EE) and economic development. This paper explores the mechanism by which digital finance impacts EE based on panel data from 30 provinces in China spanning from 2011 to 2019. The results demonstrate that digital finance can significantly enhance EE, with a particularly pronounced effect in the eastern region. Through mechanistic analysis, it is evident that GTI serves as the transmission pathway through which digital finance influences EE, accounting for 45.3% of the effect. The policy implication of this study suggests that China should expedite the digitization of financial markets to further harness the development of digital finance, particularly in pursuit of its technological innovation and green, low-carbon environmental protection effects.

Energy saving effects of digital technologies from a life-cycle-analytical perspective: evidence from China.

Digital technology has become a key driver of industrial transformation and resource utilization. However, no consensus has been reached on the exact relationship between digital technology and energy utilization. This study adopted a comprehensive index system to investigate the impact of digital technologies on energy utilization across 30 provinces in China. The results reveal a non-linear relationship between digital technologies and energy efficiency in China (represented by an N-curve), which is validated by robustness tests. This indicates digital technology exerts a fast-slow-rapid impact on improving energy efficiency throughout its initial-rapid-stable developmental stages. Geographically, this effect is more pronounced in eastern and central China, as well as in areas with lower energy efficiency. Furthermore, the impact of digital technology on total energy consumption can be characterized by an inverted N-shaped curve. As regional energy efficiency improves, the energy consumption associated with the development of digital technologies gradually decreases. These findings can contribute valuable insights for enhancing energy efficiency and provide practical guidance for the formulation of energy and digital technology policies.

The effect of superstitious beliefs on corporate investment efficiency: evidence from China

This study investigates the effect of CEOs' cultural-specific superstitious beliefs on corporate investment efficiency in China. Using data collected from Chinese listed firms from 2008 to 2021, we find that CEOs' beliefs about certain years in the zodiac year limit risk-taking behavior and thus mitigate overinvestment but aggravate underinvestment. Specifically, our findings reveal that superstitious beliefs of the zodiac impact corporate investment efficiency in two directions. For firms already in a state of overinvestment, CEOs' decision not to invest for fear of loss due to their superstitious belief of the zodiac year balances that firm's investment level, yielding a positive effect on corporate investment efficiency. However, in the case of firms that were already in a state of underinvestment, CEOs' risk-avert attitudes due to the same beliefs exacerbate underinvestment. Further, our findings show the effect of such beliefs are stronger in firms with a lower level of marketization, weaker internal control, and less external monitoring. Our findings shed light on the effect of Chinese traditional cultural factors on corporate decision making in emerging economies.

Impact of China’s financial development on the sustainable development goals of the Belt and Road Initiative participating countries

China’s Belt and Road Initiative (BRI) aims to strengthen regional economic and policy cooperation and achieve the rapid development among the participating countries. While the impact on the financial development of the economic growth and energy environment of BRI participating countries has garnered close attention among scholars, few studies focus on the impact of financial development on the sustainable development goals (SDGs) of the BRI participating countries. To address this gap, we utilized panel regression models to quantitatively assess the impact of China’s financial development scale, structure, and efficiency on the SDGs of the BRI participating countries, and adopted Geographically and Temporally Weighted Regression (GTWR) model to explore the spatial-temporal effects of China’s financial development scale, structure, and efficiency on the SDGs of the BRI participating countries. Our findings indicate that China’s financial development has significantly promoted the SDGs of the BRI participating countries. This study further reveals that the scale and efficiency of China’s financial development have had a more pronounced impact on the SDGs of Asian countries, low- and middle-income countries, and the Land Silk Road participating countries, compared to those of European countries, high-income countries, and the Maritime Silk Road participating countries, respectively. In contrast, the structure of financial development primarily promotes the SDGs of European and high-income BRI participating countries in the land silk belt. The role of China’s financial development in promoting the SDGs of most BRI participating countries has gradually increased over time. This study provides valuable insights for decision-makers in China to facilitate the sustainable development of BRI participating countries and foster a shared community within the BRI framework.

Examining the impact of urban management on regional innovation efficiency in China’s manufacturing industry with a focus on modern sustainable energy: utilizing a non-radial distance function method

This study examines the impact of urban management on the efficiency of regional innovation in China’s manufacturing industry, with a specific focus on modern sustainable energy. The research takes into consideration the regional diversity in innovation development across China’s three major regions and integrates common Frontier theory with the MSBM model. By employing a non-radial distance function approach, this study develops the MMSBMP model, incorporating various improved methods proposed by researchers. The Luenberger index methodology is utilized to assess the innovation efficiency of the national manufacturing industry from 2017 to 2021, enabling the identification of efficiency losses. The findings highlight significant disparities and opportunities for enhancing innovation efficiency across the three major regions, both at the common Frontier and the group Frontier. However, caution is advised due to the potential overestimation of regional Frontier efficiency values resulting from variations in technical reference datasets. Analysis of the Total Growth Rate (TGR) values reveals distinct development characteristics among the regions, with the eastern region exhibiting smaller extremes and the central and western regions displaying larger extremes. This comprehensive examination of China’s manufacturing industry emphasizes the influence of urban management. By investigating the consequences of urban management practices, this research provides insights into the relationship between urban development strategies and the performance, efficiency, and sustainable growth of the manufacturing industry. The study highlights the significance of urban management in shaping regional innovation efficiency and emphasizes the continual improvement of the industry’s innovation performance in the context of modern sustainable energy.

Measuring water-used and production efficiency in China using the super-efficient directional distance function

ABSTRACT This study employs the super-efficiency directional distance function (SDDF) to assess the productivity of each administrative region in China over the period 2013–2017. The focus is on exploring variations in gross domestic production and efficiency related to waste gas and wastewater discharge across regions. The inputs include labor, capitalization, energy usage, and total water consumption, with domestic gross production as the output, and total wastewater and exhaust gas discharges as unintended outputs. The findings highlight Beijing, Tianjin, Hainan, Qinghai, Guangdong, Shanghai, Jiangsu, and Shandong as the most efficient regions, while Zhejiang, Ningxia, Hunan, and others exhibit lower performance. Notably, Guangxi ranks lowest (0.631). Unlike traditional direct distance function models, the SDDF model provides a more accurate estimation of the production efficiency of all 30 administrative regions, and addresses the limitation of generating the same efficiency values of 1 simultaneously for multiple regions. The study emphasizes the need for inefficient regions to reduce water consumption and emissions and enhance productivity, offering valuable insights for policymakers in formulating environmental and production policies.

Does unfairness reduce efficiency? Within-industry CEO pay inequity and firm efficiency in China

This paper investigates the impact of CEO pay inequity within an industry on firm efficiency. We measure within-industry pay inequity as the difference in compensation between a firm's CEO and the second-highest-paid CEO in the same industry (or in the same size-industry group). Using R&D expenses and capital expenditures as the inputs, we construct data envelopment analysis (DEA) and stochastic frontier analysis (SFA) scores to evaluate firm efficiency. We find that within-industry CEO pay inequity is negatively associated with firm efficiency. We further show a significantly negative impact of within-industry CEO pay inequity on firm performance (ROA and Tobin's q). Overall, our findings highlight the destructive effect of CEO pay inequity within an industry which dampens executive motivation and leads to reduced firm efficiency, supporting relative deprivation theory and equity theory, the two major theories of perceived distributive injustice.

Cross-efficiency aggregation based on interval conditional entropy: An application to forest carbon sink efficiency

Cross-efficiency evaluation is an extension of data envelopment analysis (DEA), which can effectively distinguish between decision-making units (DMUs) through self- and peer-evaluation. The cross-efficiency of each DMU in a set of DMUs is measured in terms of intervals when the input–output data are represented by the number of intervals. Based on the interval cross-efficiency matrix, the interval entropy is defined in terms of the likelihood. Then, considering the influence of peer evaluation, the interval conditional cross-efficiency entropy is proposed and an aggregation model of the interval conditional cross-efficiency entropy is presented to create a ranking index for DMUs. Finally, a simple example is provided to illustrate the effectiveness of the proposed method, which is applied to the evaluation of forest carbon sink efficiency in China. The results indicate that the final cross-efficiencies of all 30 provinces range from 0 to 0.6. Among these provinces, those with a relatively high efficiency include Guangdong, Guizhou, Hainan, Shandong, and Qinghai.

Political governance and corporate investment efficiency in China

ABSTRACT The involvement of the Communist Party of China (CPC) in corporate decision-making has formed a governance model with ‘Chinese characteristics’ that diverges from commonly studied governance models. This paper provides direct insight into China’s political governance model by examining how the Party organization influences the investment efficiency of private firms. We find that increasing the proportion of Party directors (i.e. stronger political governance) improves the investment efficiency of private firms. This finding survives various robustness checks. The mediation analysis suggests that Party organizations promote corporate investment efficiency by playing advisory and supervisory roles in corporate governance. One policy implication is that, from the perspective of improving investment efficiency, the CPC should encourage private firms to invite more Party organization members to the board of directors, supervisory board, or management.

The Efficiency of China’s Hub Economy and Its Influencing Factors: A Two-Stage Analysis Based on the Super SBM-Malmquist-Tobit Model

Hub economy is a kind of emerging economic form. Developing a hub economy is essential to strengthen domestic and foreign connectivity and build a powerful country in transportation. This paper designs a two-stage analysis framework for the evaluation and impact study of the input-output efficiency of the hub economy based on the super SBM model, Malmquist model, and Tobit model. In the first stage, the Super SBM and Malmquist models are used to measure the static and dynamic efficiency of the hub economy. In the second stage, the Tobit model is used to analyze the factors influencing the efficiency of the hub economy. Among them, the explained variable in the second stage is the measurement result of technical efficiency in the first stage. The empirical results of 30 provinces and cities in China from 2012 to 2021 show that (1) the technical efficiency (TE), pure technical efficiency (PTE), and scale efficiency (SE) of China’s hub economy are 0.585, 0.740, and 0.820, respectively, which do not reach the effective state; (2) the technical efficiency change index (Effch), technical progress change index (Techch), and total factor productivity change index (Tfpch) of China’s hub economy are 0.994, 0.945, and 0.939, respectively, indicating that the corresponding efficiencies show a downward trend; and (3) industrial structure, innovation, and technology are significantly and positively correlated with the efficiency of the hub economy; policy and enterprises are significantly negatively correlated with the efficiency of the hub economy; and education does not correlate with the efficiency of the hub economy.

Enhancing Agricultural Ecological Efficiency in China: An Evolution and Pathways under the Carbon Neutrality Vision

Under China’s carbon neutrality vision, the integration of resource consumption and ecological protection in agricultural production requires increased attention to agroecology. This study comprehensively integrates the carbon impact within agricultural ecological efficiency (AEE). Based on the window analysis of super-SBM-undesirable and geographically and temporally weighted regression (GTWR) models, AEE and its influencing factors in China’s 31 provinces from 2000 to 2021 are measured, and then types categorized, mechanisms investigated, and optimization pathways provided. The results revealed that from 2000 to 2021, the AEE showed a growing trend, with the spatial agglomeration degree increased. Its gravity center presented a migration path of “northeast–southwest”. Driven by internal and external mechanisms, both agricultural production conditions and regional socio-economic conditions had a stronger positive effect and wider range of promotion on China’s AEE, and the positive contribution of physical environmental conditions decreased, while the inhibitory effect increased. Furthermore, this study divided three zones and emphasized that the agri-ecological power zone plays a demonstrative role in carbon sequestration and efficiency improvement, the eco-pollution balance zone needs to strike a balance between economic development and agricultural ecological development, and the agri-pollution power zone ought to reduce agricultural carbon emissions and mitigate its environmental impact.

Structural characteristics and influencing factors of a spatial correlation network for tourism environmental efficiency in China

The tourism industry in China presents uneven tourism efficiency but deepening spatial associations; thus, tourism resources must be more rationally allocated. In this study, the highly efficient SBM model was used to measure the tourism environmental efficiency of 31 provinces in China. A spatial correlation network is then constructed based on the gravity model, and the structural characteristics and influencing factors of the network are analyzed. The results show that (1) the overall tourism environmental efficiency in China presents a fluctuating growth trend, with significantly higher values observed in the eastern region than in the central and western regions; moreover, the growth in efficiency in the eastern region has been relatively stable in recent years, that in the central region has increased, while that in the western region has significantly declined. (2) A spatially linked network with a stable tourism environmental efficiency structure has been formed in China. The number of network relations and density of the network fluctuate and increase, while the network efficiency continues to decrease; however, a strong small-world nature is observed. (3) An obvious network core–edge structure is observed, with Shanghai, Beijing, Zhejiang, and Jiangsu at the center showing a significant intermediary role and remote provinces such as Tibet, Xinjiang, Ningxia, and Inner Mongolia at the edge showing fewer connections. (4) The four major plates of China based on the CONCOR algorithm are sparsely connected internally and show strong inter-plate connections and spillover effects. (5) The industry support capacity difference matrix, technological development level difference matrix, transportation accessibility difference matrix, and environmental regulation level difference matrix significantly and positively affect spatial associations, while the geographical distance matrix significantly and negatively affect spatial association relationship establishment. These findings have important theoretical and practical significance for the sustainable development of tourism in China's provinces and cities.

Retracted: Study on Measurement and Drivers of Inclusive Green Efficiency in China

Retracted: Study on Measurement and Drivers of Inclusive Green Efficiency in China

The differential impact of the digital economy on urban energy efficiency in China: the mediating mechanism of FDI

The differential impact of the digital economy on urban energy efficiency in China: the mediating mechanism of FDI

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.

How does managerial ability affect R&D efficiency of emerging-economy high-tech enterprises

ABSTRACT Numerous publications have discussed the nexus between managerial ability (MA) and innovation capability; however, regarding the hi-tech enterprises in transition economies, the nexus between MA and R&D efficiency will be discussed in depth. Considering the data from high-tech listed companies A-shares in Mainland China in 2012–2017, the data envelopment analysis (DEA) window, Tobit panel model, and panel threshold model is utilised to analyse the direct, moderating effects and threshold effect of MA on R&D efficiency of China’s high-tech enterprises. The findings illustrated that MA could significantly facilitate R&D efficiency, and this enhancement effect is interestingly more evident in state-owned enterprises. Enterprise ownership exerts a significant positive moderation effect between MA and R&D efficiency of China’s hi-tech enterprises. Both equity incentive and salary incentive exerts a significant negative moderation effect between MA and R&D efficiency of China’s hi-tech enterprises. In addition, the panel threshold model proved a nonlinear nexus between MA and its R&D efficiency. Based on the abovementioned findings, corresponding countermeasures are proposed.

A Comparative Study of the Driving Factors of Water Resources Use Efficiency in China’s Agricultural and Industrial Sectors

The efficient use of water resources has become an important topic in China. Research on measurement and driving factors is the foundation for improving water resources use efficiency (WRUE). In this paper, the super-efficiency slacks-based measure (SE-SBM) model is used to measure the WRUE of China from 2005 to 2021. The agricultural carbon emissions and chemical oxygen demand (COD) in industrial wastes are taken as undesirable by-products. The driving factors of WRUE are discussed with use of the Tobit regression model. The results show that China’s agricultural WRUE ranges from 1.185 in Jilin to 0.687 in Ningxia. In the industrial sector, the WRUE ranges from 1.399 in Beijing to Jiangxi 0.212. The economic structure and development level, water resources endowment, government influence and environmental regulation, agricultural planting scale and urbanization rate have impacts on WRUE. Precautionary measures need to be applied to prevent inefficient WRUE caused by the declining share of the industrial sector in the economic structure. More financial support should be focused on water-saving irrigation in agriculture and energy and resource efficiency in industry. The organizational structure and technological advantages of urbanization should also be emphasized in efforts to improve water efficiency.

Assessing the determinants of scale effects on carbon efficiency in China's wastewater treatment plants using causal machine learning

The debate over the merits of centralized versus decentralized wastewater treatment plants (WWTPs) has gained prominence considering pressing sustainable development objectives and the need to reduce greenhouse gas (GHG) emissions. This highlights the importance of innovative analytical tools to shape forthcoming policies. Using causal machine learning, we evaluate the impact of WWTP scale on GHG emission intensities and investigate contributing factors. Results show GHG intensity typically decreases as WWTPs scale up. However, this trend varies based on regional environmental, economic, and infrastructure elements. Specifically, regions with fewer industrial wastewater contributions, increased rainwater composition, and elevated temperatures show smaller scale effects. This suggests limited GHG reductions from merely expanding WWTPs in such areas, as the benefits of handling fluctuating inflow volumes, tackling heavy pollution, and operating in cooler conditions offered by larger WWTPs are compromised. This research lays the foundation for comprehensive models promoting sustainable wastewater treatment strategies.

Dynamic efficiency evaluation of electric power and environmental treatment efficiency in China

Dynamic efficiency evaluation of electric power and environmental treatment efficiency in China

Linkage analysis for water-energy-economic system efficiency in China

Linkage analysis for water-energy-economic system efficiency in China