Super Slacks-based Measure Research Articles

Spatial Correlation Network Characteristics and Influencing Factors of Industrial Carbon Emission Efficiency of Cities in Pearl River Basin

China has experienced rapid development of urbanization and industrialization, and urban industrial carbon reduction has become the key to green and sustainable development. Exploring urban industrial carbon emissions and its spatial correlation characteristics is of great importance for promoting collaborative cooperation between cities and achieving the "dual carbon" goal. Considering 49 cities in the Pearl River Basin as research objects, the super slacks-based measure model was used to measure the urban industrial carbon emission efficiency from 2010 to 2020, and exploratory spatiotemporal data analysis was used to explore its spatial pattern. On this basis, we used social network analysis and quadratic assignment procedure （QAP） methods to explore the spatial correlation network characteristics and influencing factors of urban industrial carbon emission efficiency. The results indicated that the industrial carbon emission efficiency of the Pearl River Basin showed a downward trend. Additionally, a strong spatial imbalance and heterogeneity was observed. The Pearl River Delta Region always dominated the spatial correlation network of carbon emission efficiency, forming a multi-level composite network system with the core network as the primary component and the conventional network as the auxiliary component. Economically developed cities, such as Guangzhou, Shenzhen, and Dongguan have long been in the center of the network and have extensive connections with other cities, while playing an intermediary role. The level of urbanization, science and technology, and industrialization have an increasingly significant positive impact on the strength of the connection between urban industrial carbon emission networks. The Pearl River Basin industrial carbon emission efficiency network has significant individual differences and hierarchical characteristics. According to the individual centrality, network role, and clustering characteristics, combined with the corresponding influencing factors, the Pearl River Basin urban collaborative carbon emission reduction strategy was formulated. This study can provide reference for the formulation and implementation of regional low-carbon collaborative development strategies.

Does regional economic development drive sustainable grain production growth in China? Evidence from spatiotemporal perspective on low-carbon total factor productivity

Contrary to conventional expectations, the impact of economic development on food green efficiency exhibits pronounced regional heterogeneity. This study utilizes total factor productivity (TFP) as a key metric, establishing a model for carbon emissions from grain production and developing a framework for measuring grain low-carbon TFP growth. Employing the undesirable super-slacks-based measure (SBM) and the Malmquist-Luenberger (ML) index, we assess the low-carbon TFP growth across 31 Chinese provinces from 2000 to 2021. The Dagum–Gini coefficient and variance decomposition are applied to analyze disparities from both regional and structural perspectives. An econometric model further validates the influence of economic development on low-carbon TFP growth. Our findings reveal that China’s technical efficiency (TE) and low carbon TE initially show a declining trend, with the gap between them narrowing over time. The eastern region consistently demonstrates high low-carbon TE, while the northeastern region lags. All regions exhibit positive low-carbon TFP growth, with efficiency change (EC) significantly contributing to this growth. Although economic development generally fosters low-carbon TFP growth, marked regional heterogeneity persists. Interestingly, certain traditionally economically developed regions, such as the eastern region, exert a significantly negative impact on low-carbon TFP growth. Balancing economic development with low-carbon TFP growth remains an ongoing challenge that necessitates effective management strategies.

The Impact of Land-Use Carbon Efficiency on Ecological Resilience—The Moderating Role of Heterogeneous Environmental Regulations

China attaches great importance to land use and ecological civilization; hence, clarifying the relationship of land use on ecological resilience is crucial for urban development. The aim of this paper is to study the impact of land-use carbon efficiency on ecological resilience and the moderating role played by different environmental regulatory policies between the two, with the aim of providing a research basis and decision-making reference for the country’s ecological high-quality development by proposing suggestions for different subjects based on the results of this study. Taking 30 provinces and cities in mainland China from 2009 to 2022 as samples, the authors constructed an indicator system to measure their ecological resilience using the entropy method, measured their land-use carbon efficiency using the super SBM, and verified the mechanism of land-use carbon efficiency on ecological resilience by using the bidirectional fixed-effects model. Robustness and endogeneity tests confirmed the validity of the regression results. The following is a summary of this study’s findings: (1) Land-use carbon efficiency can enhance ecological resilience through various mechanisms such as scale promotion, structural upgrading, and technological progress. (2) Regional research shows that different regions have distinct effects of land-use carbon efficiency on ecological resilience. The northeastern region shows a non-significant inhibitory effect, whereas the eastern, middle, and western regions show varying degrees of promotion effects. Land-use carbon efficiency contributes to increased ecological resilience in resource-based and non-resource-based provinces, with resource-based provinces witnessing a greater increase in ecological resilience. The effects of land-use carbon efficiency on different aspects of ecological resilience are diverse, with ecosystem resistance and recovery being empowered. However, the precise mechanism through which ecosystem adaptability influences ecological resilience remains unclear. (3) Moreover, there is variation in the moderating impact of environmental legislation. Command-and-control environmental regulation impedes the positive impact of land-use carbon efficiency, and market-incentive environmental regulation strengthens their relationship, while spontaneous-participation environmental regulation does not significantly enhance their connection. It provides a new theoretical perspective for the study of ecological resilience, deepens the understanding of ecological resilience, and provides theoretical support for enhancing the resilience of ecosystems.

The impact of fintech on the efficiency of urban commercial banks in China-based on super slacks-based measures with undesirable output

This study endeavours to elucidate the influence of financial technology (fintech) on the operational efficiency of urban commercial banks in China. By scrutinizing the integration of Internet-based fintech solutions, it seeks to illuminate their potential for streamlining banking processes, elevating customer experiences and augmenting the precision of credit assessments and lending decisions through sophisticated data analytics and risk management tools. This research evaluates the efficiency of Chinese urban commercial banks employing the Super Slacks-Based Measure (Super-SBM) which incorporates undesirable output. Furthermore, it leverages the System Generalized Method of Moments (System GMM) to investigate fintech’s specific impact on urban commercial bank efficiency across various Chinese regions. The findings underscore fintech’s pivotal role in catalyzing digital transformation within banks, thereby bolstering operational efficiency and customer satisfaction. Moreover, this study highlights the critical importance of fintech in fortifying risk management capabilities within the banking sector. The insights gleaned from this research contribute to the banking industry’s sustainable growth and competitive resilience. Additionally, the findings furnish policymakers with empirical evidence to support the strategic integration of fintech and the optimization of banking service processes. Urban commercial banks can streamline operations, enhance customer experiences, refine credit assessments and ultimately attain higher efficiency and long-term sustainability levels in the rapidly evolving financial landscape by harnessing the transformative potential of fintech.

Empirical Analysis of a Super-SBM-Based Framework for Wetland Carbon Stock Safety Assessment

With climate change and urbanization expansion, wetlands, which are some of the largest carbon stocks in the world, are facing threats such as shrinking areas and declining carbon sequestration capacities. Wetland carbon stocks are at risk of being transformed into carbon sources, especially those of wetlands with strong land use–natural resource conservation conflict. Moreover, there is a lack of well-established indicators for evaluating the health of wetland carbon stocks. To address this issue, we proposed a novel framework for the safety assessment of wetland carbon stocks using the Super Slack-Based Measure (Super-SBM), and we then conducted an empirical study on the Quanzhou Bay Estuary Wetland (QBEW). This framework integrates the unexpected output indicator (i.e., carbon emissions), the expected output indicators, including the GDP per capita and carbon stock estimates calculated via machine learning (ML)-based remote sensing inversion, and the input indicators, such as environmental governance investigations, climate conditions, socio-economic activities, and resource utilization. The results show that the annual average safety assessment for carbon pools in the QBEW was a meager 0.29 in 2015, signaling a very poor state, likely due to inadequate inputs or excessive unexpected outputs. However, there has been a substantial improvement since then, as evidenced by the fact that all the safety assessments have exceeded the threshold of 1 from 2018 onwards, reflecting a transition to a “weakly effective” status within a safe and acceptable range. Moreover, our investigation employing the Super-SBM model to calculate the “slack variables” yielded valuable insights into optimization strategies. This research advances the field by establishing a safety measurement framework for wetland carbon pools that leverages efficiency assessment methods, thereby offering a quantitative safeguard mechanism that supports the achievement of the “3060” dual-carbon target.

Impact of rural digital economy development on agricultural eco-efficiency: evidence from mainland China

The integration of the digital economy with rural development is of great significance as it plays a pivotal role in mitigating carbon emissions and environmental pollution in agriculture, thereby contributing to the evolution of agriculture in a green and sustainable manner. This study aims to examine the impact and mechanisms of rural digital economy development (RDED) on agricultural eco-efficiency (AEE). Specifically, based on provincial-level panel data from China spanning from 2011 to 2021, we evaluate China’s AEE by employing the super-efficiency slacks-based measure (Super SBM) model, taking into account the positive externality of agricultural carbon sinks. Then we analyze the impact and mechanisms of RDED on AEE using the two-way fixed effects model. The findings indicate that: (1) RDED significantly promotes AEE, and this conclusion remains robust even after being tested by replacing the explained variable, altering the sample interval, and including more control variables; (2) RDED can significantly drive AEE in the midwestern regions of China, but the promotion effect on the eastern region has not been fully demonstrated. Additionally, the promotion effect in southern China is greater than that in northern China; (3) agricultural science and technology investment partially mediates the impact of RDED on AEE. Moreover, agricultural science and technology innovation has a positive moderating effect on the relationship between RDED and AEE. Lastly, this study provides new evidence and policy recommendations for developing countries, such as China, to proactively facilitate the coordinated development of the rural digital economy and agricultural ecology, and attain green and sustainable ecological agriculture.

Sustainable Impact of Green Building on the Eco-Economic Efficiency of the Construction Industry: Evidence from China

Addressing the global warming challenge, the carbon emissions reduction potential of green building (GB) is garnering increasing attention. However, the extent to which GB can impact the eco-economic efficiency (EE) of the construction industry remains unclear. To fill this gap, based on panel data for 30 regions of China from 2003 to 2018, in this study, a comprehensive analysis of the EE and sustainable impact of GB is conducted using a super slacks-based measure (SBM-DEA), panel vector autoregression (PVAR) and threshold models. The findings reflect that only about 10% of provinces have achieved EE effectiveness, with pure technical effectiveness being a significant driving force behind this. The average comprehensive, pure technical, and scale EE show a 62.23%, 46.42%, and 31.91% improvement potential regarding the efficiency frontier. The EE in the Eastern Region is relatively high, while the Western Region surpassed the Central Region in regards to EE in the areas of scale and pure technical efficiency. EE is significantly reduced in the current period (year) when it is subject to the positive impact of a standard deviation from the existing economic level. This impact was the strongest in period 1, then gradually disappeared until period 6, aligning with the Kuznets curve’s theoretical assumption. GB awareness negatively impacted the current period, but is expected to gradually show a positive effect after period 2. The urbanization, green building awareness, and green building coverage make a very small contribution to the EE, accounting for 8.6%, 1.6%, and 9%, respectively, with early EE and economic level identified as the primary variables affecting the current EE. The impact of GB on EE exhibits a threshold effect, with the ecological effect of GB significant only in cities with economic levels higher than 11.063. This research contributes to the existing knowledge of the eco-economic mechanism of GB and provides insights for government policies, promoting the sustainable development of the GB market.

Spatiotemporal patterns, effects, and the interactive driving mechanisms of urban sustainability based on the eco-efficiency framework: Evidence from Chinese prefecture-level cities

Eco-efficiency is a vital tool for evaluating and advancing regional sustainable development. This study presents an urban eco-efficiency (UEE) framework that deserves widespread application. By using the Super Slacks-Based Measure model, the research calculates the UEE of 284 prefecture-level cities in China from 2005 to 2022. The study employs the Standard Deviational Ellipse to illustrate the spatiotemporal pattern of UEE in China and examines its effects using non-parametric kernel density estimation, the coefficient of variation, and both global and local Moran's I. The integration of UEE with urban development patterns, which is not immediately clear, is achieved through the entropy-based TOPSIS model. Additionally, the Malmquist index, the Two-way Fixed Effects (TWFE) model, and the Panel Vector Autoregression (PVAR) model reveal the dynamic changes and interactive driving mechanisms of UEE in China. The results indicate that: (1) UEE in China displays a consistent “core-periphery” pattern, exhibiting a notable hierarchical impact from regional, scale, and administrative perspectives. The complexity of UEE's spatiotemporal variations has increased due to the impact of COVID-19. (2) The UEE of Chinese cities shows a significant ‘club’ effect, with efficiency thresholds for high-high and low-low aggregations set at 0.8. (3) Only 25% of Chinese cities have achieved optimal UEE levels. Notably, cities in the west contribute most to national UEE growth, while midland cities contribute the least. The decreasing trend in UEE growth underscores the urgent need for a shift in China's urban development model. (4) UEE in China benefits from a resilient self-reinforcing mechanism, with urban development and economic growth significantly aiding UEE growth. Despite a non-significant negative impact from government intervention, the inhibitory effect of an intensive industrial structure on UEE turns into a promotional effect, especially following COVID-19.

Spatio-temporal characteristics of carbon emission efficiency in the tourism regions of China’s three northeastern provinces

ABSTRACT The three provinces of northeastern China are significant tourist destinations. The associated carbon emission issue in this region serves as a scientific foundation for the development planning of tourism. There is a lack of research into tourism carbon emission efficiency (CEE). This study collected the relevant data between 2010 and 2020 to gauge CEE in the three northeastern provinces using the super-slack-based measure (SBM) model. The spatio-temporal characteristics of CEE were analysed using the ML index and Moran’s I index. The findings revealed that, between 2010 and 2020, the tourism CEE of the three northeastern provinces followed the order of Heilongjiang < Jilin < Liaoning, with an overall trend of initially decreasing and then increasing.

Analysis of regional differences and evolution features for waterway transport efficiency in the Yangtze River Economic Belt considering undesired outputs

Waterway transport plays an increasingly important role in pursuing the sustainable development of the transportation industry and promoting regional economic development. Understanding the regional differences and evolution features of waterway transport efficiency (WTE) is a requisite for formulating waterway transport development policies. However, many studies of transport efficiency mainly focus on road, railway, and air transportation. Few studies have conducted an in-depth analysis of the WTE in the Yangtze River Economic Belt (YREB) from the perspective of considering undesired output. Adopting the Super slack-based measure (Super-SBM) model, Dagum Gini coefficient, and kernel density estimation as an analysis tool, this study explored the WTE’s region disparities and dynamic evolution features based on the data from 2011 to 2020 in YREB. The analysis results represent that: (1) there represented a fluctuating and slowly upward trend of waterway transport efficiency during the study period. (2) The midstream region has the highest waterway transport efficiency, followed by the upstream region, and last is the downstream region. (3) The regional disparities in waterway transport efficiency exist in YREB, and the primary source of these differences is the inter-regional difference. And (4) the polarization phenomenon and agglomeration characteristics of waterway transport efficiency in YREB are gradually weakened and decreased, respectively. In the end, this article summarized the implications of this study to improve the waterway transport efficiency and service capabilities in YREB.

Evaluating Carbon-Emission Efficiency in China’s Construction Industry: An SBM-Model Analysis of Interprovincial Building Heating

In the pursuit of China’s ambitious carbon neutrality goals, optimizing carbon-emission efficiency within the construction sector, a significant emitter, becomes critical. This study employs a super-Slacks-Based Measure (SBM) model and a Tobit regression model to analyze buildings’ heating-related carbon emissions across China, considering urban population density, electricity usage, and building energy consumption and the influencing factors that cause differences in carbon-emission efficiency difference. The results of this study show that the average building carbon-emission efficiency of 30 provinces in China is 0.789; carbon-emission efficiency is 0.89 in the south, higher than 0.69 in the north. After excluding centralized heating emissions, the value of buildings’ carbon-emission efficiency in the northern provinces increases by 0.01, of which the buildings’ carbon-emission efficiency in Jilin Province and Ningxia Hui Autonomous Region shows positive growth, respectively, by 0.12 and 0.17. In terms of influencing factors, there is a significant positive correlation between the scientific and technological levels, the regional economic scale, and carbon-emission efficiency; however, government intervention in the economy has a negative correlation with carbon-emission efficiency. Renewable energy utilization and green-policy adoption emerge as pivotal in enhancing efficiency. The contribution of this study is to underscore the necessity of fostering renewable energy, refining energy-consumption structures, and implementing green strategies to augment buildings’ heating-related carbon-emission efficiency.

Research on the characteristics and influencing factors of the spatial correlation network of cultivated land utilization ecological efficiency in the upper reaches of the Yangtze River, China.

Researching the structural characteristics of the spatial correlation network of cultivated land utilization ecological efficiency is of great significance to China's food security and agricultural green and low-carbon development. Taking 47 cities (autonomous prefectures) in the upper reaches of the Yangtze River as the research object, the ecological efficiency of cultivated land utilization from 2010 to 2020 was measured based on the unexpected output model (Super SBM), and the spatial correlation matrix was constructed using the revised gravity model. The structural characteristics of the spatial correlation network were analyzed using the social network model (SNA), and finally, the factors affecting the spatial correlation network of cultivated land utilization ecological efficiency in the upper reaches of the Yangtze River were analyzed through the quadratic assignment procedure (QAP) model. The results show that: (1) the ecological efficiency of cultivated land utilization in the upper reaches of the Yangtze River has been increasing year by year, but the overall level is low, and there is a large gap among provinces. Sichuan Province has the highest average value of 0.605, and Yunnan Province has the lowest average value of 0.359. (2) The ecological efficiency of cultivated land utilization in the upper reaches of the Yangtze River has broken through the provincial boundaries and has formed an obvious spatial correlation network, but the overall density is low, and the network is still relatively loose, needing further development and improvement. Chengdu, Yibin, Luzhou, and other cities are located in the center of the network and have formed four cohesive subgroups. (3)The differences in the level of agricultural economic development, the rural per capita disposable income, the differences in agricultural mechanization intensity, the regional population differences, and spatial adjacency have an impact on the spatial network of ecological efficiency of cultivated land utilization in the upper reaches of the Yangtze River. The difference in the level of agricultural economic development, the rural per capita disposable income, and the differences in agricultural mechanization intensity are negatively correlated, while the regional population differences are positively correlated with spatial adjacency.

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.

Evaluation of educational resource utilization efficiency, regional technological heterogeneity, and total factor productivity change in 35 European countries.

Educational resource utilization efficiency (ERUE) and productivity growth are considered current global challenges that the modern world faces. This study evaluates the educational resource utilization efficiency, dynamic productivity change, and regional discrepancies in technologies involved in educational resource utilization in 35 European countries and four regions. DEA Super SBM, Meta frontier analysis, and Malmquist productivity index are employed to gauge the ERUE, technology gap ratio (TGR), and total factor education resources productivity change. A set of inputs and outputs is used from 35 European countries for the study period of 1998-2021. Results revealed that the average ERUE in European countries is 0.6312, Which indicates a 36.88% improvement potential in educational resource utilization. Southern Europe continuously exhibits superior average ERUE scores (0.6871) compared to other regions, indicating a higher efficiency in using educational resources. Luxembourg (1.0813), Czechia (0.9356), and Slovenia (0.8984) are found to be the top three performers in terms of ERUE level. The technology gap ratio value is highest in Southern Europe. It demonstrates that southern European countries used the most advanced technology in education resource utilization. The average Malmquist Index (MI) in European countries is 1.0349. It Indicates a 3.49% growth in educational resource utilization. Technology is the primary determinant of productivity growth, as Technological change is higher than efficiency change. Southern European countries showed the highest MI of 1.0542. Italy, Lithuania, and Serbia were found to have higher average MI scores over the study period (1998-2021). Finally, the Kruskal Wallis test proved that ERUE and TGR in 4 different regions of Europe are heterogeneous. In contrast, the MI in European regions isn't found to be significantly different.

Analysis of the spatial correlation pattern of logistics carbon emission efficiency and its influencing factors: the case of China.

As logistics carbon emission efficiency is an essential industry linking regions, investigating this issue from a spatial correlation perspective is practically significant. Utilizing data from 282 prefecture-level cities spanning 2006 to 2019, we used a super slacks-based measure model, a modified gravity model, motif analysis, the Infomap algorithm, and an exponential random graph model to analyze the spatial correlation patterns and influencing factors of logistics carbon emission efficiency. The following conclusions were drawn. (1) The spatial correlation of logistics carbon emission efficiency during the study period exhibited a core-edge pattern, with the central region emerging as a high-correlation hub. (2) The scale of the spatial association network community of carbon emission efficiency in the logistics industry changed constantly, and the stability of the network community structure gradually increased. From a microstructural perspective, the dispersed-mode structure was a pivotal element in the formation of the spatial correlation network of logistics carbon emission efficiency. (3) Node interaction tendencies were a critical force driving network formation. Financial investment, government concern, international openness, population density, and innovation ability were conducive to the formation of spatial correlations of logistics carbon emission efficiency.

Efficiency of higher education financial resource allocation from the perspective of ‘double first-class’ construction: A three-stage global super slacks-based measure analysis

Efficiency of higher education financial resource allocation from the perspective of ‘double first-class’ construction: A three-stage global super slacks-based measure analysis

Calculation and analysis of agricultural carbon emission efficiency considering water–energy–food pressure: Modeling and application

Agricultural carbon emission efficiency (ACEE) measurement, a tool for effectively achieving sustainable development goals, has garnered much attention. However, the impacts of resource pressures such as water, energy and food on ACEE have been overlooked, and the high dimensionality of the measurement model and insufficient sample data can easily distort the measurement results. Therefore, from a green development perspective, we established a new ACEE measurement framework considering the water–energy–food pressure index and a new integrated ACEE measurement model (CSMA-PPE-USSBM) that includes chaotic maps, the slime mould algorithm (SMA), projection pursuit evaluation (PPE) and the undesirable super slack-based measure (USSBM). The model was used to calculate ACEE in 13 prefecture-level municipalities in Heilongjiang Province, China, and analyze its spatiotemporal evolution and possible causes. The results showed that this model avoids the above problems. The reliability coefficient and stability coefficient reached 0.962 and 0.971, respectively; ACEE in Heilongjiang Province has much room for improvement, but there are obvious differences in carbon emission efficiency in different carbon emission type regions. The key driving forces of ACEE variation can generate significant scale effects. Provincial driving factors can affect ACEE variation in prefecture municipalities, where the influence range is limited or the influence of driving factors gradually emerges. The research results provide a theoretical reference for accurately measuring the regional ACEE and analyzing the driving mechanism of ACEE and green agricultural development.

Regional differences and dynamic evolution of agricultural water resources utilization efficiency in China.

Improving water resources utilization efficiency is conducive to achieving the sustainable development of water resources. It is essential to explore the regional differences and dynamic evolution of agricultural water resources utilization efficiency in China to promote high-quality development of agriculture. In this study, based on the unexpected output, we build a super slack-based measure model to measure agricultural water resources utilization efficiency in China's provinces from 2007 to 2018. In addition, we use the Dagum Gini coefficient to analyze the source of regional differences. Finally, we construct the distributed dynamics model to explore the distribution of the dynamic evolution trend of China's agricultural water resources utilization efficiency. The results reveal that regional difference is the main source of the overall difference in agricultural water resources utilization efficiency in China. Spatial imbalance exists in the development of agricultural water resources utilization efficiency in China. The agricultural water resources utilization efficiency in various provinces and regions of China is relatively stable, having the characteristics of club convergence. The probability of maintaining the initial state is high, and the internal mobility is low. However, with time, the degree of club convergence decreases.

A Two-Stage Dea Model of Super Sbm and Malmquist for Analyzing Innovation and Energy Sustainability Efficiency of Asian Countries

The relationship between innovation and energy sustainability is deeply interconnected, as innovation plays a crucial role in achieving long-term solutions for energy challenges. Innovation involves the introduction of new concepts, products, or techniques that drive economic progress and enhance quality of life. On the other hand, energy sustainability focuses on ensuring the availability of cost-effective and reliable energy resources while mitigating environmental impacts. In Asia, the most populous and diverse region in the world, the correlation between innovation and energy sustainability holds significant importance. The region faces the complex task of balancing rapid economic growth and urbanization with the imperative to adopt sustainable energy practices. To assess performance and identify best practices, two key global indicators, namely the Global Innovation Index (GII) and the Energy Trilemma Index (ETI), can provide valuable benchmarks. This study aims to evaluate the efficiency of innovation and energy sustainability among 32 Asian countries using a two-stage Data Envelopment Analysis (DEA) model that incorporates the Super Slacks-based Measure (Super SBM) and Malmquist approaches. Additionally, hierarchical cluster analysis will be applied to group these countries based on their efficiency scores. By examining efficiency levels, influential factors, and the impact of the innovation-energy sustainability relationship on economic growth, this research can assist policymakers in making informed decisions. Furthermore, this study seeks to contribute to the existing body of knowledge by conducting a comprehensive investigation into the relationship between innovation and energy sustainability in the Asian region. Keywords: efficiency; innovation; energy; GII; ETI; sustainability; DEA; Super SBM; Malmquist; clustering; Asia

Assessing Asian Countries' Competitiveness: Two-Stage Dea Analysis of Global Innovation Index and Logistics Performance Index Integration

Competitiveness has emerged as a crucial economic characteristic in policy and academia. It refers to the combination of institutions, policies, and factors that determine a country's level of productivity by assessing different aspects of competitiveness. This paper aims to assess the competitiveness of Asian countries by combining Global Innovation Index (GII) and Logistics Performance Index (LPI). The authors implement an integrated model called the Data Envelopment Analysis Super Slack-Based Measure Model (DEA–Super SBM) and the Malmquist Model (DEA–Malmquist) to assess competitiveness from 2012 to 2018. The study begins by validating seven critical factors from the GII and the LPI that impact competitiveness. Subsequently, the Super-SBM model evaluates the competitiveness efficiency for 30 Asian countries during the specified time frame. The DEA–Malmquist model is then employed to analyze the overall change in countries' competitiveness productivity across Asia. This research suggests that most Asian countries have achieved competitiveness efficiency, with countries such as China, Pakistan, and Kuwait demonstrating high indicators. However, a few countries still exhibit relatively low competitiveness indicators, and there has been some variation in competitiveness efficiency over the years. The study's implications can provide valuable insights to governments and policymakers, aiding in developing effective strategies to strengthen competitiveness and foster economic development. Keywords: efficiency; inefficiency; competitiveness; GII; LPI; Data Envelopment Analysis; Malmquist; Super Slack-Based Measure; Asia