Total Factor Productivity Change Research Articles

Which productivity can promote clean energy transition —total factor productivity or green total factor productivity?

Developing clean energy is a key pathway and an inevitable choice for achieving the goals of carbon peak and carbon neutrality. From a global perspective, technology is increasingly affecting the trajectory of energy transition, driving clean energy into a stage of rapid development. Therefore, this paper focuses on exploring the dynamic evolutionary characteristics of clean energy transitions driven by different productivity. Using panel data from 171 economies from 1990 to 2019, this paper systematically examines the impact of Total Factor Productivity (TFP) and Green Total Factor Productivity (GTFP) on clean energy transitions. The empirical results indicate that both TFP and GTFP positively impact clean energy transition. Specifically, clean energy consumption increases by 3.35% and 6.03%with a one standard deviation change in TFP and GTFP respectively. Upon decomposing TFP and GTFP, it was found that Green Efficiency Change (GECH) and Green Technical Change (GHCH), especially GECH, are the main factors driving the clean energy transition. Heterogeneity analysis shows that, in developed economies, GTFP has a larger positive impact on clean energy transition than TFP. Furthermore, GTFP demonstrates a significant positive impact on the clean energy transition both before and after the 2008 financial crisis, whereas TFP's positive impact is only evident before the crisis. Our findings emphasize the social benefits of further investments in GTFP.

Impacts of Capital Formation and Investment Sources on Total Factor Productivity: The Example of Nigeria’s Agriculture

The study was designed to analyse the impact of capital formation and investment sources on Nigeria's agricultural productivity from the period 1980 - 2021. The time series data were analysed with Malmquist Data Envelopment Analysis (DEA) and the tobit regression models using DEA, the Total Factor Productivity Change, which quantifies the degree of productivity, was estimated at 1.022, which implies an average productivity progress of 2.2% annually. Again, since the value of technical efficiency change is less than technological change, it implies that the productivity gains are mainly attributable to technological progress instead of efficiency improvements in the periods under the study. The results of tobit regression indicate that domestic capital formation significantly influences agricultural TFP, highlighting the importance of government expenditure on agriculture (GEA) and gross fixed capital formation (GFCF) in enhancing productivity, although with minimal effects. Human capital formation (HCF) also showed positive impacts on Total Factor Productivity TFP, suggesting that improvements in workforce skills and knowledge are crucial for agricultural efficiency. However, FDI exhibited no significant correlation with TFP, implying that local investments are more beneficial than foreign aids in the context of Nigeria's agricultural sector. The findings emphasize the need for robust policy frameworks that align capital disbursements with agricultural production periods to optimize TFP. This study contributes to the discourse on agricultural productivity by providing empirical evidence on the critical role of capital investments in enhancing TFP within developing economies like Nigeria.

Rising Tides of Knowledge: Exploring China’s Higher Education Landscape and Human Capital Growth

AbstractThis study examines the status of human capital in China’s higher education system by analyzing the quantity of higher education institutions as a metric. The results show a general enhancement in human capital in all regions, along with an optimized structure. This is demonstrated by a decline in the primary education demographic, a consistent growth in secondary education, and a rapid surge in the higher education demographic. The percentage of junior college students has remained steady, whereas the ratios of undergraduate and graduate students have been consistently increasing. The Malmquist index method is used to analyze changes in total factor productivity (TFP) among provinces and regions by breaking it down into technical level and efficiency changes. From 2005 to 2010, there was a general increase in total factor productivity (TFP) and technical proficiency, although technical efficiency showed only slight enhancement. TFP and technical level growth decelerated from 2011 to 2020, while technical efficiency stayed above 1. These findings indicate that China’s total factor productivity (TFP) growth is mainly driven by technological advancements, with minimal improvements in technical efficiency.

An evaluation of the regional heterogeneity of museums' operational efficiency and productivity changes across Chinese provinces

Museums are critical in safeguarding cultural heritage and cultivating community educational opportunities. This research aims to evaluate operational efficiency (OE), the impact of technological change on total factor productivity change (TFPC), and the regional heterogeneity of museum performance in three regions and 31 provinces across China. To this end, the study employed DEA-SBM and the Malmquist Productivity Index to gauge OE, TFPC, and determinants of TFPC (efficiency change or emerging technologies change) across 31 provinces for 2012–2021. Results reveal that the average OE of the Chinese Museum is 0.8394. It shows a 16.06 % growth potential in the operational efficiency of Chinese Museums. Further, the OE of Chinese Museums declined over the study period from 0.8965 in 2012 to 0.8088 in 2021. Beijing, Fujian, and Hunan are ranked top with a Museum's OE Score of 1. The average MI score of Chinese Museums is 0.9744, and technology change is the main determinant of Decline in productivity change as EC = 0.9992 is greater than TC = 0.9846. The MI of Liaoning, Shanghai, Ningxia, Jiangxi, Chongqing, Sichuan, Guangdong, and Tianjin is over 1, indicating growth in total factor productivity over the study period. The eastern region of China shows higher operational efficiency and total factor productivity scores of museums than the central and western regions. The results of the Kruskal-Wallis test proved that a statistically significant difference exists among different regions of China for the OE, MI, EC, and TC of museums.

Can performance-based financing improve efficiency of health centers in Ethiopia? A Malmquist Productivity Index analysis

IntroductionThe Ethiopian government has introduced several healthcare financing reforms intending to improve efficiency. Piloting implementation of performance-based financing is one of these actions. The purpose of this research is to assess the efficiency of healthcare facilities that have implemented performance-based financing compared to those that have not.MethodsEfficiency was measured using a nonparametric data envelopment analysis and the Malmquist Productivity Index technique. Total factor productivity change, technical change, and technological change are compared across eight sampled healthcare facilities that are implementing performance-based financing and eight that are not in Ethiopia.ResultsHealth facilities implementing performance-based financing have a mean technical efficiency score of 64%, allowing for a potential 36% reduction in inputs without affecting outputs. Their scale efficiency is 88%, indicating a potential 12% increase in total outputs without expanding facilities. In contrast, facilities not implementing performance-based financing have a mean technical efficiency score of 62%, with a potential for 38% input reduction without affecting outputs. Their scale efficiency is 87%, suggesting a potential 13% increase in total outputs without scaling up facilities. Among the 16 healthcare facilities observed, seven experienced a decline in the mean total productivity, while one remained stagnant. The remaining eight facilities witnessed an increase in productivity. The healthcare facilities implementing performance-based financing showed a 1.3% decrease in mean total productivity during the observed period. Among them, five showed an increase and three showed a decrease in the total factor of productivity. The mean total factor of productivity of all healthcare facilities not implementing performance-based financing remained stagnant over the three-year period (2019–2021), with four showing an increase and four showing a decrease in total productivity.ConclusionsThe study concludes that implementing performance-based financing did not improve productivity levels among healthcare facilities over three years. In fact, productivity decreased among the facilities implementing performance-based financing, while those not implementing it remained stagnant. This shows health facilities that implement performance-based financing tend to utilize more resources for similar outputs, contradicting the anticipated efficiency improvement.

The effect of COVID-19 pandemic on the efficiency of training and research hospitals in Turkey

The Coronavirus disease 2019 (COVID-19), which emerged in December 2019, caused an increase in the demand for healthcare services globally. Hospitals’ operational and financial performances are thought to have been negatively affected by COVID-19 because they had to postpone providing non-urgent healthcare services to prevent infection. This study aimed to evaluate how the COVID-19 pandemic has affected the efficiency of training and research hospitals in Turkey. The study population comprised 51 training and research hospitals. The Malmquist Total Factor Productivity index was used to examine the changes in the efficiency of the training and research hospitals between 2017–2018, 2018–2019, 2019–2020, and 2020–2021. In addition, a repeated-measures ANOVA test was used to evaluate whether a significant difference existed in the efficiency levels of the hospitals in the pre-COVID-19 pandemic periods (2017–2018 and 2018–2019) and during them (2019–2020 and 2020–2021). No significant difference was found between hospitals' technical and scale efficiency levels before and during the pandemic, but the change in total factor productivity was significant (F = 34.18; p < 0.001). Our research findings provide clues about how hospitals' behaviours changed during the pandemic compared to before the pandemic. It was observed that efficiency decreases in hospitals at the onset of the pandemic disappeared with the adaptation process to the new normal. Technical efficiency decreased in two periods (2017–2018 and 2020–2021) when total factor productivity increased. Reverse changes in total factor productivity and technical efficiency indicate that technological change drives total factor productivity. This study identified differences in hospital efficiency during both ordinary and extraordinary periods. It would benefit managers and policymakers to recognise these differences and focus on managerial skills that ensure technical efficiency in extraordinary circumstances to mitigate possible efficiency losses.

Efficiency and productivity gains of robotic surgery: The case of the English National Health Service.

This paper examines the effect of new medical technology (robotic surgery) on efficiency gains and productivity changes for surgical treatment in patients with prostate cancer from the perspective of a public health sector organization. In particular, we consider three interrelated surgical technologies within the English National Health System: robotic, laparoscopic and open radical prostatectomy. Robotic and laparoscopic techniques are minimally invasive procedures with similar clinical benefits. While the clinical benefits in adopting robotic surgery over laparoscopic intervention are unproven, it requires a high initial investment cost and carries high on-going maintenance costs. Using data from Hospital Episode Statistics for the period 2000-2018, we observe growing volumes of prostatectomies over time, mostly driven by an increase in robotic-assisted surgeries, and further analyze whether hospital providers that adopted a robot see improved measures of throughput. We then quantify changes in total factor and labor productivity arising from the use of this technology. We examine the impact of robotic adoption on efficiency gains employing a staggered difference-in-difference estimator and find evidence of a 50% reduction in length of stay (LoS), 49% decrease in post-LoS and 44% and 46% decrease in postoperative visits after 1year and 2years, respectively. Productivity analysis shows the growth in radical prostatectomy volume is sustained with a relatively stable number of urology surgeons. The robotic technique increases total production at the hospital level between 21% and 26%, coupled with a 29% improvement in labor productivity. These benefits lend some, but not overwhelming support for the large-scale hospital investments in such costly technology.

Digitalization as a Factor of Production in China and the Impact on Total Factor Productivity (TFP)

In the digital transformation era, digitalization integrates deeply into production, bolstering output efficiency and economic value. Through stochastic frontier analysis (SFA), this research positions digitalization as an input in the production function, dissecting its elasticity impact on capital, labor, and output. The effect of digitalization on total factor productivity change (TFPC) is explained by comparing TFPC with and without digitalization. Findings reveal that digitalization’s integration into economic growth displays a U-shaped trajectory, with initial productivity setbacks transitioning to long-term benefits as industries adapt. The periodic complementarity and substitution between digitalization and labor, along with a weak substitution relationship with capital, illustrate that, as a production factor, digitalization dynamically interacts with other factors, both complementing and substituting them. This dynamic interplay highlights the intricate role that digitalization plays within the production function. Furthermore, digitalization has played a crucial role in China’s TFP growth, which also highlights the lack of other technological progress. Meanwhile, the pace of digital transformation presents scalability challenges, evident in the fluctuating scale efficiency change (SEC). Policymakers are advised to address these early stage challenges through supportive measures, ensuring smoother digital transitions. Concurrently, industries should embrace this non-linear transformation, emphasizing adaptability to maximize digitalization’s long-term advantages.

Efficiency Analysis of Life Insurance Companies in Indonesia and Malaysia

This study focuses on measuring the performance of life insurance companies in Indonesia and Malaysia. Input oriented Data Envelopment Analysis (DEA) uses panel data to extract the efficiency score of 5 life insurance companies from Indonesia and 5 life insurance companies from Malaysia for the year of 2015-2019. DEA-based Malmquist Total Factor Productivity (TFP) Index used to measure the efficiency change along the study period. This study utilizes asset and operating expenses as input variables, as for output variables, this study utilizes premium and investment income. Based on an efficiency score computed by DEA, most of the life insurance companies in both countries perform efficiently during the study period. However, life insurance companies in Indonesia still perform more efficiently than Malaysian life insurance companies. The results of the Malmquist TFP Index for life insurance companies in both countries show that there is 2.5% improvement in technical efficiency change, 1.5% deterioration in technology efficiency change, 0.6% improvement in pure efficiency change, 1.9% improvement on scale efficiency change, and 0.9% increase in TFP change. It shows that the main reason for TFP increased is due to improvement of technical efficiency change.

Labor Mobility Networks and Green Total Factor Productivity

Population migration continues to reshape the spatial pattern of China’s population and regional economic development. During this internal migration process, production and consumption patterns often change, ultimately leading to changes in green total factor productivity. This paper, based on the Chinese population census data and 1% sampling survey data from 2005 to 2015, utilizes social network analysis methods to measure the labor mobility network indicators of 284 prefecture-level cities. Further, this paper analyzes the impact and mechanisms of regional network status on green total factor productivity using a panel fixed effects model. We find that as network density increases, the interpersonal connections between regions become closer, and the network exhibits a clear pattern of “concentrated inflows” and “dispersed outflows”, with the trend of forming strong alliances becoming increasingly apparent. Regions positioned centrally either in terms of network in-degree or out-degree exhibit higher green total factor productivity. Among these, the labor mobility network plays a crucial role in enhancing green total factor productivity through the channel of technology diffusion effects, which improve investment efficiency via knowledge exchange and material capital accumulation. The promotive effect of labor network status on green total factor productivity is more pronounced in the eastern regions, where talent quality is higher, and in areas with fewer restrictions from the household registration system.

The impacts of technological change policies on water uses and virtual water trade in water-constrained settings

Virtual water trade, which underlines the importance of inter-regional commodity transactions for enhancing water management, has often been recommended as an alternative to technological advancements in addressing water scarcity and increasing water savings in dry regions. Sectoral adjustment policies that focus on enhancing the productivity of specific sectors therefore should prioritize sectors with high growth impact potential but low virtual water demand. However, the diverse understanding of the concept and differing assessment techniques employed by various research communities complicate the debates surrounding the policy relevance of the virtual water trade. This study proposes a novel and integrated approach to evaluating virtual water content and flows endogenously, leveraging the advantages of Input-Output and Computable General Equilibrium (CGE) modeling methods. The quantification of the method is exemplified in the case of Uzbekistan (Central Asia), where water is a limited yet essential resource for sustainable economy and environmental systems. Specifically, this study examines the impacts of changes in economy-wide and sector-specific Total Factor Productivity (TFP) and water productivity on water uses, virtual water flows, and economic outcomes. The findings reveal that TFP improvements in livestock and fodder crop production and water productivity enhancements in gardening are recommendable options to constrain the net virtual water trade and enhance economic growth in Uzbekistan. The study also shows a high sensitivity of virtual water flows to technological changes, highlighting the importance of considering options for improving productivity when analyzing the potential of virtual water trade.

Digital Financial Services (DFS) And Productivity of Indian Banking Sector - Empirical Evidence Using Malmquist Productivity Index and Panel Data Regression

Purpose: The aim of this study is to examine the impact of Digital Financial Services (DFS) on the productivity of banking sector in India. Theoretical framework: This research considered various digital banking services offered by bank and how it affects the actual bank performance in terms of productivity, by adopting a two-stage model i.e., Malmquist Productivity Index (MPI) and panel data regression. Design/Methodology/Approach: The empirical study was based on eight-year balanced panel data from 2012 to 2020. The sample of the study consists of forty-four commercial banks from India. This study is completely based on secondary data collected from the website of the database of the Indian economy and the National Payment Corporation of India (NPCI). To achieve the research goals, a two-stage approach has been used. Initially, Malmquist Productivity Index (MPI) was employed to estimate the total factor productivity changes. In the second phase, panel regression analyses were used to study the impact of Digital Financial Services (DFS) on bank productivity. Findings: The findings show that the Digital Financial Services (DFS) variables such as mobile banking, online banking, Automatic Teller Machines (ATM) and Point of Sale (POS) transactions are significantly improved the productivity of the Indian banking industry. Research, Practical &amp; Social implications: The study addresses the issues such as identifications of factors affecting the productivity of banks including Digital Financial Services (DFS). In the world of digital revolution, it analyses whether bank can retain, continue and enhance their performance by offering modern product and services to their customers. Originality/Value: This article has conducted extensive analyses of Digital Financial Services (DFS) and banks' productivity. The authors also provide suggestions for the policymakers for the future implementation of digital banking services.

The Euro Area 12: A Comparative Assessment of Its Member States in the Period 1998–2022

The primary objectives of this research are to compare the economic performance of the Member States (MS) in the Euro Area 12 (EA-12) of the European Union (EU) that served as net contributors and net recipients of its budget during the period 1998–2022. The comparison focuses on aspects related to economic growth and the business cycle, exploring the presence of hysteresis. To achieve these objectives, a novel approach is employed, which integrates the analysis of both growth and cycles. This approach involves using the Seemingly Unrelated Regression Equations (SURE) model to estimate a translog production frontier and assess the impact of lagging output gaps on the potential output or capacity for each of the MS considered. The data used to estimate each frontier and analyze growth decomposition consist of quarterly data filtered using the Kalman filter. The results reveal that lagging output gaps, occurring one or two quarters prior, significantly affect the potential output or capacity of the EA-12 MS, thereby supporting the existence of hysteresis. However, the impacts appear to be minimal. Furthermore, the research concludes that changes in total factor productivity (TFP), mainly due to technical change, play a more crucial role in the growth of MS that are net contributors to the EU budget. Conversely, total factor accumulation (TFA), mainly physical capital accumulation, has a more pronounced impact on the growth of MS net recipients from the EU budget. Interestingly, the heterogeneity observed among the EA-12 MS extends beyond the simple division between net contributors and net recipients of the EU budget. This heterogeneity raises questions about the European Central Bank’s (ECB) ability to stabilize the economies of these various MS solely through conventional monetary policy.

How Important is Mobile Broadband Latency for Total Factor Productivity Growth?

This paper investigates the relationship between the log change in mobile broadband latency and total factor productivity (TFP) growth based on data for 130 countries. It finds that there is a strong negative correlation between TFP growth and one year lag of latency growth in OECD countries. The interpretation of the findings is that a 10 percentage points decrease in the growth of latency in period t-1 is associated with an increase of 0.3 percentage points in TFP growth. The findings are in accordance with the framework of General Purpose Technologies that suggests that the impact of new technologies often appear with a lag. Moreover, no relationship is found for the total sample or for non-OECD countries. One possible explanation could be that OECD countries have reached a higher maturity in digitalisation and automation in production processes and thus are able to take advantage of the benefits of lower latency.

Regional efficiency and total factor productivity change for small and medium sorghum producers in Zambia: A slacks-based measure and Malmquist index approach

Zambia's efforts to promote countrywide production of sorghum is backed by a number of apropos factors. It is not only an adaptive response to the climate change challenge, but also a dietary diversity improvement measure, to achieve food and nutrition security. Further, improved sorghum production will supply emerging agro-industries, especially specialty foods, beverages and stock feed manufacturing in the country. However, regional sorghum production efficiencies in Zambia are not well known. This paper attempts to evaluate regional sorghum production technical efficiencies (TE), as well as Total Factor Productivity (TFP) change from 2011 to 2022. To study these aspects, we apply non-parametric Data Envelopment Analysis (DEA) techniques such as the Slacks-Based Measure (SBM), and the Malmquist Productivity Index (MPI), followed by bootstrap OLS regression respectively. Cross-sectional estimates from the SBM model for the 2022 cropping season indicate that average TE for small and medium sorghum farmers in Zambia was 85 percent. Sorghum production could be expanded by 411.90 t by improving TE in five of ten provinces in Zambia. The MPI shows that TFP for sorghum production in Zambia declined by 21 percent, mainly due to deteriorating sorghum production technology. Populations, rurality, agro-ecological zone IIB are the factors associated with EC and TFP decline.

Technical Efficiency Evaluation of Maize Production in Ghana: An Application of Three-Stage Data Envelopment Analysis

Purpose: In Ghana, maize constitutes the primary source of revenue and food for small-scale farmers, and to attain maize self-sufficiency, appropriate production techniques must be employed, coupled with the scaling up of production efficiency to supplement our food output. The objectives of the study are to estimate the technical efficiency and analyze the trend in total factor productivity of maize production. Method: This study focuses on forty-eight (48) cities in six (6) selected regions of Ghana: Northern, Upper East, Upper West, Central, Eastern, and Volta. Secondary data on maize production was obtained from the Ministry of Food and Agriculture from the 2010/11 to 2020/21 cropping seasons. Environmental variables (amount of rainfall and temperature) were sourced from the Ghana Meteorological Agency (GMA) from 2010/11 to 2020/21. Results and Conclusion: The results displayed a fairly high level of TE, and environmental factors significantly influence production efficiency. The study revealed that the TE of maize cultivation was 0.752 and 0.853 for the first and third stages, respectively. From the findings, the efficiency scores attained after the adjustments to the input in the third stage of the DEA model were greater than those in the first stage. Also, the results indicated the eastern region had the highest TE scores, showed an increment in productivity, and serves as the finest region for maize cultivation. Overall, the technical performance of maize cultivation had shown an upward trend in productivity, and the eastern region recorded the highest efficiency performance and technological progress. Implications of the Research: The research highlights the significance of the maize crop to the Ghanaian economy and how environmental factors impact farmers' production efficiency. It also emphasizes the benefits of resource use efficiency and its implied effects on the total factor productivity of maize cultivation. Policy suggestions include sustainable production practices primarily for farmers living in susceptible areas to enhance productivity and improve their food security status. Originality/Value: The gap discovered in the examined literature was that there exists a substantial deficiency in the usage of the three-stage DEA Malmquist model for analyzing technical efficiency and limited studies on the estimation of the total factor productivity (TFP) changes, specifically focusing on maize farming. This paper adds to the body of the existing literature by utilizing a three-stage DEA model to evaluate the TE of maize production. This is entirely limited both in theory for annual crops and methodologically in the Ghanaian literature on efficiency studies.

Natural resources utilization efficiency evaluation, determinant of productivity change, and production technology heterogeneity across developed and developing G20 economies

Efficiently utilizing natural resources (NRs) and enhancing their productivity growth are crucial factors for sustainable economic growth in major global economies. Further production technologies heterogeneity among developed and developed G20 countries is vital to natural resource utilization efficiency (NRUE). To this end, the estimation of NRUE, natural resources total factor productivity, and technological gap ratio (TGR) is worth investigating. This study employed Data Envelopment Analysis (DEA) Super-SBM, Meta-frontier analysis, and the Malmquist productivity index to evaluate NRUE, TGR, and productivity changes among G20 countries from 1995 to 2020. The average Natural Resource utilization Efficiency (NRUE) among G20 countries is 0.7071, indicating an improvement potential of around 29.29% in natural resource conversion. Developed G20 economies have a natural resource utilization efficiency (NRUE) of 0.9528, higher than that of the NRUE (0.506) of developing G20 countries. Developed countries have a TGR of 0.9962, which is significantly higher than the TGR of developing countries at 0.6746, indicating a significant gap in production technology. Total factor productivity growth of 0.8 % was witnessed in developed countries while developing countries demonstrated a decline of 0.6 percent in Total Factor Productivity Change (TFPC) over the study period. Analyzing the decomposition of TFPC reveals technological change as the predominant factor influencing productivity change in G20 economies instead of efficiency changes. A Mann-Whitney U test has proved the existence of significant statistical differences in average NRUE, TGR, and technological change (TC) levels between developed and developing G20 countries.

Assessment of low-carbon tourism development from multi-aspect analysis: a case study of the Yellow River Basin, China

Climate change has become an unavoidable problem in achieving sustainable development. As one of the major industries worldwide, tourism can make a significant contribution to mitigating climate change. The main objective of the paper is to assess the development level of low-carbon tourism from multi-aspect, using the Yellow River Basin as an example. Firstly, this study quantified tourism carbon dioxide emissions and tourism economy, and analyzed their evolution characteristics. The interaction and coordination degree between tourism carbon dioxide emissions and tourism economy were then analyzed using the improved coupling coordination degree model. Finally, this study analyzed the change in total factor productivity of low-carbon tourism by calculating the Malmquist–Luenberger productivity index. The results showed that: (1) the tourism industry in the Yellow River Basin has the characteristics of the initial environmental Kuznets curve. (2) There was a strong interaction between tourism carbon dioxide emissions and tourism economy, which was manifested as mutual promotion. (3) The total factor productivity of low-carbon tourism was increasing. Based on the above results, it could be concluded that the development level of low-carbon tourism in the Yellow River Basin has been continuously improved from 2000 to 2019, but it is still in the early development stage with the continuous growth of carbon dioxide emissions.

Financial resources utilization efficiency in sports infrastructure development, determinant of total factor productivity growth and regional production technology heterogeneity in China

Despite China's heavy investment in sports infrastructure development in the last decades, the financial resources utilization efficiency (FRUE), regional technological gap (TGR), and total factor productivity change (TFPC) in sports infrastructure development are undiscovered and worth investigating. To this end, this study employed DEA-SBM, Meta-frontier analysis, and Malmquist productivity index on the data set of 31 Chinese provinces and 3 regions for the years 2014–2021 to gauge the FRUE, TGR, and TFPC in sports infrastructure development. The results indicate that the average FRUE is 0.4859, with a growth potential of 51.41% in financial resource utilization efficiency for sports infrastructure development. Further Eastern region is more efficient as compared to the Central and western regions. Beijing, Shanghai, Tibet, Hainan, and Guangdong are top performers in FRUE. Further, the value of TGR in the East is 0.9787 which is higher than in Central (0.4977), and Western (0.5821) regions. It indicates that the eastern region contains superior production technology in the development of the sports infrastructure in China. Moreover, the average TFPC value is 1.035 witnessed a 3.5% growth, primarily determined by technological change (TC). As TC = 1.0273 is higher than efficiency change EC = 0.997. The eastern region has a higher average TFPC of 1.048, indicating higher total factor productivity growth in sports infrastructure development. Beijing, Liaoning, Tianjin, Shanghai, and Zhejiang are the top performers in the TFP growth over the study period. Finally, the Kruskal-Wallis test proved the statistically significant difference in three regions of China for FRUE, TGR, and TFPC in sports infrastructure development.

A Study on the Changes of Green Total Factor Productivity in Chinese Cities under Resource and Environmental Constraints

Confronting the dual challenges of excessive resource consumption and environmental pollution, the traditional extensive economic development pattern significantly impeded the high-quality development of the Chinese economy. Examining variations in green total factor productivity across different types of cities holds substantial practical significance for promoting coordinated regional development and facilitating the green transformation of urban economies. Panel data from 283 cities in China spanning the years 2006 to 2020 were selected for analysis. The window-Malmquist–Luenberger index model, incorporating a mixed distance function, was employed to assess changes in green total factor productivity among the sample cities. The results were then categorized and analyzed based on different city attributes. The findings indicate that (1) the variation in green total factor productivity across China’s four major regions from 2006 to 2020 is generally characterized by an initial decline followed by an increase; (2) the proportion of cities with significantly improved green total factor productivity decreases from the east to the central, western, and northeastern regions; (3) the increase in green total factor productivity is positively correlated with city size, suggesting that larger cities experience higher growth in green total factor productivity; (4) first- and second-tier cities exhibit a relatively high mean value of green total factor productivity growth, while third-, fourth-, and fifth-tier cities demonstrate relatively lower growth.