Non-radial Directional Distance Function Research Articles

Static High-Quality Development Efficiency and Its Dynamic Changes for China: A Non-Radial Directional Distance Function and a Metafrontier Non-Radial Malmquist Model

Static High-Quality Development Efficiency and Its Dynamic Changes for China: A Non-Radial Directional Distance Function and a Metafrontier Non-Radial Malmquist Model

Collaborative management of environmental pollution and carbon emissions drives local green growth: An analysis based on spatial effects

Collaborative management of environmental pollution and carbon emissions (CMPC) has been a major policy instrument to promote Sustainable Development Goals (SDG) in recent years. However, the relationship between the benefits and drawbacks of this environmental management practice for green growth in and around a local area remains to be clarified. Using 30 provinces in China during 2001–2019 as the object of analysis, we assessed the efficiency of local CMPC practices using the nonradial directional distance function (NDDF) model, predicted local green growth using the frontier green complexity index (GCI), and empirically examined the spatial effects, locational heterogeneity, and threshold characteristics of the relationship using the spatial Durbin model and the panel threshold model. Our study finds that although efficient CMPC does drive local green growth, the promotion effect is nonlinear with decreasing marginal effect. This effect is particularly obvious in economically developed regions with higher CMPCs, which will absorb resources from neighboring regions and create a "siphoning" effect. It was found that local financial support and foreign direct investment (FDI) can radiate green growth to neighboring regions; therefore, CMPC practice needs to pay more attention to the effect of joint governance, supplemented by financial and foreign investment policy tools, to better promote the green transformation of local economy.

Scenario Analysis of CO2 Reduction Potentials from a Carbon Neutral Perspective

As a major emitter of CO2, China needs to take responsibility for slowing down global warming. In this paper, the potential carbon emission intensity of provinces is firstly calculated using the non-radial directional distance function under the group- and meta-frontier techniques, and then six scenarios based on two factors (economic development and carbon intensity) are set up to estimate the emission reduction potential of China and each province. Considering the goal of carbon neutrality, the calculation of CO2 emission reduction potential quantifies the amount of emissions that can be reduced and the amount of emissions that should be balanced. Additionally, the degree of difficulty in achieving abatement potential is also calculated. The findings are as follows: First, assuming that the economic growth rate is reduced to 4.4% (achieving the second “100-year goal”) and each province adopts the most advanced low-carbon technologies, China could reduce carbon emissions by 5970.56 Mt compared to 2019 levels. To achieve net-zero emissions, the remaining 3824.2 Mt of carbon emissions should be removed by carbon reduction technologies. Second, the effect of slowing down economic growth and decreasing carbon intensity varies greatly among provinces. Hebei and Shandong should be prioritized as they have the greatest potential for emission reductions under both scenarios. Third, it is more difficult for Beijing, Shanghai, Hubei, Hunan, Inner Mongolia Autonomous Region, Chongqing, and Sichuan to achieve the abatement potential and they require more effort to reduce the same amount of carbon emissions compared to other provinces. The study provides a reference for achieving carbon neutrality and helps provinces to develop differentiated emission reduction strategies.

How can open public data promote efficient and equitable green production? Evidence from eco-efficiency in China.

Nowadays, concurrent attention to economic development and ecological issues is becoming an important trend. In this paper, we measure the eco-efficiency of 285 Chinese cities from 2003 to 2019 using a non-radial directional distance function and the data envelopment analysis method, based on which we analyze the club convergence of cities' eco-efficiency using the logt test; we estimate the impact of open public data platforms on eco-efficiency and its convergence using a multi-period difference in difference model and panel-ordered logit model, respectively. We find that, first, open public data platforms improve cities' eco-efficiency by about 6.5%, and the impact mechanisms include scale efficiency, technical efficiency, and total factor productivity, or, at the micro level, increasing the economic agglomeration degree, boosting the amount of foreign investment used, and increasing green innovation level. Second, there are three convergence clubs of eco-efficiency in China's cities, whose average eco-efficiency trends are above, close to, and below average, respectively. Third, public data platforms significantly increase the probability of cities belonging to the convergence clubs of high and medium eco-efficiency (Clubs 1 and 2) and decrease the probability of belonging to the low one (Club 3). However, the mechanisms only include technical efficiency and total factor productivity, or the amount of foreign investment used and the green innovation level at the micro level.

The impact of green finance on renewable energy development efficiency in the context of energy security: Evidence from China

The influence of green finance (GF) on renewable energy development efficiency (REDE) has garnered considerable attention from both academia and society. To study the changes in China's REDE in recent years and GF's effect on it, this research first evaluates 30 provinces’ REDE from 2010 to 2019 using a non-radial directional distance function (NDDF) based on global technical assumptions. Second, we evaluate the direct impact of GF on REDE. Finally, we explore the internal mechanism for how GF influences REDE. The results of this study are as follows. (1) GF has a significant promoting effect on REDE. (2) The improvement of renewable energy (RE) technological innovation level and the increase of foreign investment strengthen GE's promoting effect on REDE. (3) The positive effect of GF on REDE is prominent in regions with lower environmental regulations and carbon intensities and higher financial development levels. This paper offers relevant policy suggestions to improve REDE and to enhance GF development.

Marginal abatement cost of CO2: A convex quantile non-radial directional distance function regression method considering noise and inefficiency

By purchasing emission reduction equipment or reducing production scale, it is possible to effectively decrease CO2. Therefore, understanding the marginal abatement cost (MAC) associated with these methods is crucial for making decisions. However, previous studies using the directional distance function (DDF) approach often mis-specified production functions and neglected data noise. They also assumed decision-making units (DMUs) to be on the production frontier and used proportional changes in inputs and outputs as abatement paths. This paper addresses these limitations by developing the convex quantile non-radial directional distance function (CQR-NDDF) method, which estimates the MAC of CO2 and determines optimal abatement paths for DMUs without assuming a specific production function, employing linear programming techniques. Applying this method to 30 provinces in mainland China from 2011 to 2019, the study finds that China's CO2 MAC increased from 182 to 247 yuan/ton. The lowest-cost abatement path varies by province and time. The club convergence and ordered probit model are employed to conclude that the second industry and urbanization increase the MAC of CO2, while factors such as foreign direct investment, openness level, and human capital decrease the MAC. Moreover, the CQR-NDDF method yields significantly lower MAC estimates than the NDDF method. In conclusion, this paper provides new insights into China's CO2 MAC, emphasizing the importance of considering inefficiency and data noise in MAC estimation. We anticipate that utilizing CO2 MAC as a benchmark for carbon trading market prices could lead to an increase in prices within China's carbon trading market.

The impact of water rights trading on water resource use efficiency: Evidence from China's water rights trading pilots

With the increasing conflict between the supply and demand of water resources, enhancing water resource use efficiency has gradually become a focus of attention. As a new way to manage water resources, water rights trading pilots have been implemented in China. It is important to explore the impact of water rights trading on regional water resource use efficiency and possible impact paths. This paper employs a global nonradial directional distance function (GNDDF) model to measure water resource use efficiency, which is more accurate and reliable. To discern the effect of China's water rights trading pilot policy on enhancing regional water resource use efficiency, we apply the regression control method (RCM) for counterfactual analysis and test the possible pathways through which the water rights trading policy affects water resource use efficiency. This article draws the following conclusions. (i) China's overall water resource use efficiency has not reached a high level over the past decade, and water resource use efficiency has exhibited substantial regional disparities. (ii) There is some evidence to suggest that the water rights trading policy may have a positive impact on improving water resource use efficiency in the pilot provinces. However, the improvement effect exhibits regional variations, which are likely attributed to the initial water resource use levels in the pilot provinces and the specific implementation modes of water rights trading. (iii) Water rights trading policies can enhance water resource use efficiency through two pathways, including facilitating reclaimed water reuse and promoting water use restructuring.

The energy saving effects of digital infrastructure construction: Empirical evidence from Chinese industry

Digital infrastructure serves as a crucial foundation for development in the era of the digital economy. In light of this, this paper takes digital infrastructure construction as its focal point. Utilizing Python technology and the Meta-frontier non-radial directional distance function to calculate the level of digital infrastructure construction and industrial energy efficiency. It then employs a dynamic panel data model to analyze the impact effects and mechanisms of digital infrastructure construction on industrial energy efficiency. Additionally, threshold models and modified gravity models are employed to explore the nonlinear effects and spatial impacts of digital infrastructure construction on industrial energy efficiency. The research findings reveal that the enhancement of digital infrastructure significantly improves industrial energy efficiency. Moreover, indicate a shift in the impact of digital infrastructure construction on industrial energy efficiency from inhibitory to promotional as the level of digital infrastructure construction increases. The mechanism of this effect suggests that digital infrastructure construction can promote the improvement of industrial energy efficiency through green technological innovation and the advancement of industrial structure. Further analysis shows that the spatial network characteristics of digital infrastructure construction notably elevate the level of industrial energy utilization efficiency. This study provides practical policy implications for sustainable development.

A good medicine tastes bitter: Environmental regulation that shapes China's green productivity

This study scrutinizes the effect of the Sewage Charges System (SCS) reform on the green total factor productivity (GTFP) across China's provinces. Employing the global Luenberger productivity indicator based on a non-radial directional distance function, we observe an upward trend in China's GTFP during the study period. However, the SCS reform precipitates a contraction in the GTFP, particularly pronounced in low-income and resource-based provinces of China. Empirical findings withstand a battery of robustness checks of self-selection and placebo tests. Further mechanistic tests suggest that the SCS reform influences the GTFP by escalating pollution treatment costs, curtailing R&D investment, and undermining the quality of green innovation via crowding-out effects. Overall, the SCS reform is effective in curbing SO2 emissions, but it reduces economic efficiency. Therefore, how to synergize emission reduction and economic growth is an important issue to be addressed in the area of environmental policy design in China.

Environmental performance evaluation of electric enterprises during a power crisis: Evidence from DEA methods and AI prediction algorithms

With the continuous occurrence of power crisis events worldwide, meeting society's demand for electricity has kept coal-fired power generation high, which leads to a large amount of carbon dioxide emissions and environmental pollution problems. Therefore, improving the environmental performance of thermal power plants under the background of a power crisis has become particularly important. This research provides a new path for production optimization and environmental performance improvement of electric power enterprises from the perspective of combining prediction algorithms and DEA methods. We apply the modified non-radial directional distance function (MNDDF) method to a set of panel data containing 16 different cogeneration units and 27 different periods in eastern China and construct the grey prediction model to predict the future environmental performance of these units. Empirical analysis shows that our model has high prediction accuracy and offers a reasonable basis for future production decision-making adjustments for electric power enterprises. Based on the results herein, we present valuable suggestions for thermal power plants to improve their environmental performance during a power market crisis, including optimizing resource utilization, improving management level and operating conditions, strengthening technological innovation, and enhancing production factor allocation.

Centralized allocation of flexible measures: A non-radial directional distance function DEA approach

Centralized allocation of flexible measures: A non-radial directional distance function DEA approach

Environmental Protection Tax and Energy Efficiency: Evidence from Chinese City-Level Data

The aggravated global warming and energy crisis have greatly challenged the healthy and sustainable development of society worldwide. Improving energy efficiency is one of the vital ways to overcome the dilemma. Existing studies explore the impact of environmental regulation on energy efficiency; however, the potential impact of the environmental protection tax (EPT) on urban energy efficiency has received little attention. Using the panel dataset of 278 Chinese cities from 2011 to 2019, the unified efficiency index (UEI) based on a total non-radial directional distance function (TNDDF) is first used to calculate urban energy efficiency. A difference-in-differences (DIDs) model is conducted to explore the impact of the EPT policy on the urban UEI and its potential mechanisms. The findings indicate that: (1) The average UEI in cities experienced an uptrend and a downtrend during 2011–2019. The overall UEI levels were low, especially in Jiaxiaguan, Tianshui, and Huyang cities. (2) The EPT policy significantly increases energy efficiency for the heavily polluting cities by approximately 5.21% more than that of the non-heavily polluting cities. (3) Heterogeneity analysis shows that EPT has a better effect on improving UEI in higher-level economic and non-resource-based cities. (4) Mechanism analysis implies that EPT boosts the urban UEI by stimulating urban green technology innovation, upgrading the industrial structure, and introducing foreign direct investment. This study offers empirical evidence and implications for policymakers using EPT to achieve higher urban energy efficiency and sustainable targets.

Oil price distortion and its impact on green economic efficiency in China’s transportation: A spatial effect perspective

China's oil prices have been supervision for a long time, which has led to oil price distortion (OPD) to some extent. To figure out whether OPD will impede the sustainability of energy, economy, and environment (measured by green economic efficiency (GEE)), this research first estimates OPD from 2003 to 2019. Then, using a non-radial direction distance function under nonconvex technology assumptions, this research calculates the provincial GEE by incorporating capital, labor, energy, and environment efficiencies into a unified framework. Furthermore, the OPD-GEE nexus and their influencing channels are analyzed using the spatial Durbin model. The results indicate that there are serious OPD and the average GEE in the transportation is relatively low. Local OPD correction can improve local GEE but OPD correction in spatially related regions will cause local GEE loss. The heterogeneity analysis implies that the GEE promotion effect resulting from local OPD correction is greater in oil-rich regions, new energy vehicle pilot regions, and regions with water transport. Furthermore, the mechanism analysis implies that OPD correction can improve local GEE through optimizing energy structure, mitigating resource mismatch, and promoting green technological progress; meanwhile, OPD correction in spatially adjacent regions can cause local GEE losses by exacerbating their resource mismatch. This study offers a new understanding of the impact of OPD on GEE and contributes to the promotion of sustainable development through market-oriented price reforms.

Government intervention, industrial structure, and energy eco-efficiency: an empirical research on new energy demonstration in cities

This study investigates the relationships among government intervention, industrial structure, and energy eco-efficiency (EE). Energy eco-efficiency was measured based on a non-radial directional distance function for 236 cities in China from 2005 to 2019. Additionally, the difference-in-difference model (DID) method and spatial econometric models were used to analyse the impact of government intervention and industrial structure on energy eco-efficiency and their spatial spill-over effects. Government intervention includes fiscal expenditures and policy orientation for new energy demonstration construction. Our results indicate that: China’s EE has a fluctuating upward trend and increased 17.85% in the period, and its spatial distribution imbalance gradually developed into a regional distribution balance. Moreover, government intervention and adjustment of the industrial structure improved urban energy eco-efficiency by 7.43% and 0.92%, respectively, which also has spatial spill-over effects in neighbouring regions. Furthermore, economic development, technological innovation, and foreign direct investment enable EE. However, urbanisation hinders the improvement of energy eco-efficiency. Finally, heterogeneity analysis showed that the policy of the new energy demonstration city has better effects on eastern and western cities in promoting EE.

Local government competition, new energy industry agglomeration and urban ecological total factor energy efficiency: A new perspective from the role of knowledge

The contradiction between industrial agglomeration and energy constrains the development of cities. Government competition can affect the flow of factors for industrial agglomeration. Based on the data of 274 cities in China from 2005 to 2020, this paper constructs improved non-radial directional distance function (NDDF) and dynamic spatial durbin model (SDM). The paper further uses a threshold effect model and a mediating effects model to examines the impact and spillover effects of local government competition (LGC), new energy industry clustering and their integration on ecological total factor energy efficiency (ETFEE) from the perspective of the role of knowledge. To solve the problem of endogeneity, this paper uses the presence or absence of highway entrances and exits in cities in 2004 as an instrumental variable. The study found that new energy industry agglomeration and LGC can promote ETFEE in the short term, but it will inhibit ETFEE in the long term. New energy industry agglomeration affects local ETFEE through two types of mediating effects: narrowing knowledge breadth and widening knowledge distance between cities. At the same time, new energy industry agglomeration and LGC has a significant spatial spillover effect on urban ETFEE in the short term. Areas with high urban ETFEE can promote the improvement of ETFEE in neighbouring areas. However, from the perspective of regional differences, it finds that the concentration of new energy industries and LGC have different effects on ETFEE. There is a significant impact in the eastern region, but not in the central and western regions. Finally, the paper gives constructive advice for the adaptation of the knowledge structure and the improvement of energy efficiency through the national and governmental levels.

Agricultural tridimension pollution emission efficiency in China: An evaluation system and influencing factors

We innovated traditional one-sided agricultural carbon emission efficiency research, comprehensively explored the agricultural tridimension pollution emission efficiency (ATPEE), constructed an ATPEE evaluation system considering technological heterogeneity characteristics based on the meta-frontier nonradial directional distance function (NDDF) model in a total-factor framework, evaluated the ATPEE in 30 mainland China provinces from 1997 to 2021 for the first time, and empirically studied the factors influencing the ATPEE in China with the Tobit model. The results showed the following: (1) ATPEE improvement potentials of 75.16 % and 50.88 % occur under the meta and group frontiers, respectively. (2) The eastern region represents the potential optimal agricultural tridimension pollution emission technology, while the central and western regions exhibit a large gap with the national potential optimal technology level. (3) The ATPEE loss in the eastern and western regions mainly results from management inefficiency, while that in the central region largely results from technology gap inefficiency. (4) The effects of the industrialization rate, urban–rural income gap, agricultural production structure, financial support for agriculture, natural conditions and effective irrigation rate on the ATPEE in the different regions vary.

The impact of digital infrastructure on energy-environmental efficiency: empirical evidence from China

Frontier studies have focused on the environmental performance of traditional infrastructure, but have generally neglected the effects and mechanisms of digital infrastructure on energy-environmental efficiency. This study attempts to use fixed effect models and mediating effect model based on panel data from 30 provinces in China from 2010 to 2017 to assess the impact of digital infrastructure on energy environmental efficiency and identify its mechanism. The non-radial directional distance function is used to measure energy environmental efficiency. The empirical results show that digital infrastructure promotes energy-environmental efficiency, which remains robust after a series of tests. Technological progress and energy industry advancement are the pathways through which digital infrastructure affects energy-environmental efficiency. Furthermore, we find that the positive effect of digital infrastructure on energy and environmental efficiency is significant in the east and where factor mismatch is high. Therefore, policymakers should develop digital technology and enact various environmental policies to effectively increase the construction of digital infrastructure, promote investment in technology research and development, accelerate the energy technology progress, and improve energy efficiency.

Evaluating the synergistic effect of digitalization and industrialization on total factor carbon emission performance

Combating climate change and reducing carbon dioxide emissions are serious challenges shared by countries around the world. In the current era, digitalization has a significant impact on CO2 emissions. However, prior studies have not assessed the synergy between digitalization and industrialization on carbon emission performance. The principal component analysis and non-radial directional distance function (NDDF) are used to measure the digitalization and total factor carbon emission performance of Chinese 245 prefecture-level cities from 2003 to 2019. This study establishes a fixed effects model to study the panel data. The findings are as follows: (1) Digitalization can significantly promote Chinese cities' CO2 emission reduction. This result still holds after several robustness checks. (2) The heterogeneity results indicate that digitalization mainly improves central cities’ carbon emission performance. Meanwhile, the impact of digitalization is more obvious after 2011. (3) Digitalization improves urban carbon emission performance through energy efficiency, industrial transformation, and technological innovation. (4) It is worth noting that digitalization synergizes with industrialization to improve carbon emission performance in Chinese cities. This study provides empirical evidence and some constructive policy recommendations for the government to push the collaborative development of the digitalization and low-carbon economy.

Does digitalization support green transition in Chinese cities? Perspective from Metcalfe's Law

In the digital era, digitalization brings great opportunities and challenges for cities' environmental governance and economic growth. However, it is worth noting that the impact of digitalization on cities' green transition has not been studied from the perspective of Metcalfe's Law. This paper uses the principal component method and non-radial directional distance function (NDDF) to calculate the digital development level and the green economic performance of 269 Chinese cities from 2003 to 2019. (1) The empirical findings demonstrate that digitalization has become a strong driver for cities' green economic transformation. (2) Digitalization indirectly promotes green transition through industrial structure optimization and green technological innovation. (3) The heterogeneity results show that the positive effect of digitalization on green development is more obvious in eastern and economically developed cities. (4) Using the partially linear functional-coefficient model (PLFC), this paper studies the non-linear nexus between digitalization and green economic transition from the perspective of Metcalfe's Law. Thus, Metcalfe's law is confirmed in the green transition of Chinese cities. Some policy recommendations are offered for realizing the coordinated development of digitalization and greening of the urban economy.

Can green transformation finance contribute to urban carbon emission performance? An empirical analysis based on a spatiotemporal bi-fixed SPDM model.

"Carbon peaking" and "carbon neutrality" are serious challenges at this stage, and the function of the green transformation finance (GTF) in the operation of carbon dioxide emission reduction is an urgent issue to be explored. However, empirical studies on GTF and carbon emission performance (CEP) are lacking. Given that, this research employs a panel dataset of 278 cities in China spanning the years 2006 to 2020 to measure the CEP of Chinese cities using the non-radial directional distance function (NDDF) approach and applies the SPDM and the intermediary effect model to practically analyze the spatial effects and mechanisms of GTF in promoting CEP. The findings derived from the empirical analysis indicate that (1) the level of GTF and CEP shows a trend of year-on-year increase, and there is a significant spatial correlation, but the absolute difference in CEP between cities increases. (2) GTF promotes the improvement of CEP in local cities. It reveals a favorable spillover impact on enhancing the neighboring cities' CEP. Furthermore, the research findings remain pertinent even after conducting rigorous tests to ensure their robustness. (3) Regional heterogeneity has been observed in the impact of GTF on CEP, with the development of GTF significantly contributing to the amelioration of CEP of neighboring cities in the east, significantly contributing to the improvement of local CEP of central cities and simultaneously contributing to the improvement of local and neighboring cities' CEP in the west. (4) The findings of the intermediary examination show that GTF mainly promotes CEP through the positive incentive effect of promoting green innovation in technology and the reverse push-back effect of optimizing the structure of industries. (5) The spatial spillover effect of GTF on CEP is more sensitive under the geographic distance matrix, showing a "three-stage" decay characteristic from near to far, and its influence range is roughly 300km. Therefore, China should strengthen the development of GTF, cross-city communication and cooperation to boost the accomplishment of the carbon neutrality objective.