Data Envelopment Analysis Model Research Articles

Weight Restriction Approach in a Two-stage Network Structure: A DEA-based Approach

The standard data envelopment analysis model aimed to maximize the efficiency scores using the most favourable input/output weights. In two-stage network structures, the presence of intermediate undesirable measures leads to a distinct domain of weight restriction study. In the presence of undesirable intermediate measures and equipped with non-cooperative game theory, this study developes a joint weight restriction approach to derive a common set of weights. Concurrently, the overall efficiency of the system can be evaluated. Numerical instances of 34 OECD countries in 2012 have been presented to elucidate the details and applicability of the proposed method.

Research on the path of technological innovation and resource allocation optimization of state-owned enterprises from the perspective of ecological environment and biomechanics

In the evolving global landscape, competition among nations has increasingly centered on the allocation of innovation resources, which are crucial for enterprises to implement development strategies under supply-side reforms. The efficiency and rationality of resource allocation directly affect the innovation capacity and operational performance of enterprises. This paper examines the relationship between technological innovation and resource allocation in state-owned enterprises through the lens of the ecological environment, employing data from multiple sources and utilizing the Data Envelopment Analysis (DEA) model to assess resource allocation efficiency. The study reveals that the proportion of R&D personnel invested in enterprises in China increased significantly from 46.35% to 73.38%, while the proportion in research institutions and universities declined to 11.48% and 11.36%, respectively. These shifts underscore the growing dominance of state-owned enterprises in driving technological innovation. The findings highlight that aligning technological innovation mechanisms with ecological sustainability not only enhances innovation capabilities and competitiveness but also accelerates industrial development and contributes to the sustainable growth of the national economy.

A Regional Efficiency Assessment of Long-Term Care Services in Taiwan

Taiwan is currently an aging society and will be a super-aging society in the near future. The purpose of this research is to use two models of data envelopment analysis (DEA)—the slacks-based measurement (SBM) model and the dynamic slacks-based measurement (DSBM) model—to analyze the efficiency of long-term care (LTC) in Taiwan. This analysis aims to explore the current situation of LTC in Taiwan and provide policy recommendations for LTC. The computation empirical result on the LTC efficiency score presents that the DSBM model exhibits higher efficiency than the SBM model after considering the carry-over variable in the former model. The result from the SBM model indicates that Taiwan’s outlying islands display the worst LTC efficiency, but this result does not appear in the DSBM model. Lastly, these two models both indicate that the number of elderly people being serviced in institutions exhibits higher efficiency and lower slack than those serviced in homes in 2017 and 2018. This paper concludes that the DEA approach is a viable method for examining the performance of the LTC services system as Taiwan approaches a super-aged society.

An integrative study on the green cultural industry and its determinants in Jiangsu province, China under the cultural revitalization initiative: a global perspective

IntroductionsThe cultural industry is pivotal in promoting sustainable economic development. This study aims to evaluate the economic efficiency of the cultural industry in Jiangsu Province by establishing a Data Envelopment Analysis (DEA) model and analyzing influencing factors using the Tobit model, all within the broader context of China’s cultural industry.MethodsA DEA model was developed to assess the economic efficiency of the cultural industry across Chinese provinces, allowing for a comparative analysis of performance. The Tobit regression model was utilized to investigate factors influencing these efficiency outcomes, with a particular emphasis on inter-provincial comparisons to understand the position and challenges faced by Jiangsu’s cultural industry.ResultsDespite Jiangsu’s cultural industry ranking among the largest in scale nationally, its economic efficiency is only moderate, consistently experiencing diminishing returns to scale. The study identifies low scale efficiency, small enterprise size, and suboptimal urbanization processes within the province as the main issues.DiscussionThe inefficiencies highlighted by the DEA model suggest a misalignment between the scale of operations and the economic outputs in Jiangsu’s cultural industry. Urbanization emerges as a crucial factor, with current practices not sufficiently supporting the growth potential of the cultural sector.ConclusionBased on these findings, the study proposes targeted policy recommendations for Jiangsu, including avoiding blind scale expansion, adjusting industrial structures, encouraging enterprise consolidation and optimization, and identifying new growth areas to better support the cultural industry’s development and contribute to sustainable economic progress.

Analysis of Sustainable Efficiency of Freight Transport in Major European Economies: An Integrated Multi-Region Input-Output and DEA Approach

This paper integrates the multi-region input-output model (MRIO) and data envelopment analysis (DEA) methods to analyze the freight transport efficiency in Europe. Social, economic, and environmental influences were combined into a sustainable efficiency rating of the freight transport sector of Germany, France, Italy, Spain, and the Netherlands. First, the freight transport sector's carbon footprint (CFP) was quantified using the MRIO model. The lifecycle-based CFP emissions of freight transport activities were assessed using a dataset from 2000 to 2018. Nineteen stochastic model-based MRIO lifecycle assessments were built for each country. Secondly, sixty instances of DEA models were created using a linear program for each mode in the selected countries. Thirdly, the sustainable efficiency scores were determined for each freight transport mode in each country over four periods: 2000–2004, 2005–2009, 2010–2014, and 2015–2018. The results illustrate that the sustainable efficiency score of inland, water, and air transport modes ranged from 0.38 to 1.

Benchmarking the medication efficiency and technological progress of diabetes drugs

BackgroundDiabetes poses a serious global challenge, given its increasing prevalence, detrimental effects on public health, and substantial economic burden. Since 1950s, tens of drugs have been approved by the United States (US) Food and Drug Administration (FDA). In the past decade, the medical community and regulatory agencies have moved away from the glucose-centric paradigm and increasingly call for a holistic approach to assess different treatments’ benefits and harms.ObjectiveThis study aimed to assess the medication efficiency and technological progress of Type 2 Diabetes (T2D) drugs, by considering their physiological outcomes, including both benefits (i.e., glucose lowering and weight loss) and adverse effects (mortality), relative to dosing frequency.MethodsTo derive medication efficiency, this study utilized data from the US FDA and prominent meta-analyses. Given that both the benefits and adverse effects of medications are multidimensional, this study employed a nonparametric frontier method, the data envelopment analysis (DEA) model, to integrate these factors into a measure of medication efficiency. Physiological outcomes could assume both positive and negative values. Adverse effects were regarded undesirable outputs. The DEA model was built under the framework of directional distance function and was able to handle negative and undesirable values which naturally arose in the case of T2D medications.ResultsThe paper presented a ranking of 20 T2D drugs in terms of medication efficiency. Three of them were able to attain the highest medication efficiency, all of which were in the GLP-1 class, including oral Semaglutide, subcutaneous Semaglutide and Dulaglutide. However, the other two GLP-1 drugs, Lixisenatide and Liraglutide, were less efficient. The average medication efficiency of drugs approved post-2010 was significantly higher than pre-2010 drugs. High dose frequency, low HbA1c reduction and insignificant weight loss were the main driving factors behind inefficiencies. Overall, medication efficiency provided an alternative perspective on treatment effectiveness other than conventional measures such as cost-effectiveness.

The impact of FinTech/Blockchain Adoption on Corporate ESG and DEI Performance

Monitoring a company's efficiency is one of its primary responsibilities. There are many approaches in our contemporary society that either use IT or the conventional technique. Methods for measuring efficiency fall into three primary categories: parametric, nonparametric, and ratio indicators. We prioritize a firm's inputs and outputs when choosing metrics to measure efficiency. Establishing objectives and goals in entrepreneurship necessitates a thorough comprehension, appreciation, and knowledge of sustainability, and assessing the economic growth quality of a corporation is an essential task for theoretical and empirical sustainability assessment. When measuring the efficiency of entrepreneurship in terms of achieving desired values of macroeconomic indicators (e.g., sustainable economic growth objectives), data envelopment analysis (DEA), a widely used technique in efficiency analysis, has taken into account the economic, environmental, and social impact of entrepreneurship as the three dimensions of sustainability. The objective of this paper is to test the influence of FinTech/Blockchain adoption on corporate ESG and DEI performances using a novel DEA approach for sustainable development assessment. It highlights the significance of using a scalable technique for ESG efficiency study and gives scholars a more thorough viewpoint on the subject. In a collection of 50 enterprises, a DEA model was utilized for the analysis. For sustainable performance assessment using the proposed DEA technique, we defined as inputs six financial metrics, and as outputs 11 ESG/Blockchain adoption, and four DEI quality metrics to measure the firm’s efficiency. The annual business data was gathered between 2017 and 2023. In all country situations we discovered that, when DEI initiatives mediate, there is a strong correlation between ESG corporate performance and the quality of economic growth (particularly in the innovation and integrity blockchain adoption performance success metrics). Our study provides additional in-depth details on the FinTech/blockchain adoption environment in comparison to the findings of previous researchers. The sustainable entrepreneurship performance (a latent variable regarded as a dependent target factor) is calculated using eight (8) factors as observed variables, which is the first to consider the dynamics of ESG/BCA and DEI quality metrics as DEA outputs. The study also examines the mediating role of DEI corporate initiatives. By conducting an empirical investigation, the suggested scalable framework makes it evident which company is more efficient in moving toward sustainability, assisting corporate management in increasing the effectiveness of economic growth.

A Six Sigma and DEA Framework for Quality Assessment in Banking Services

This study proposes a methodology that combines Six Sigma and Data Envelopment Analysis (DEA) to measure the quality of banking services. The proposed framework emphasizes seven essential quality dimensions: prompt response, efficient channels, fraudulence, processes, dependable service, credibility, customer satisfaction, and risk management. Integrating both techniques enables a holistic approach to quality evaluation and provides valuable information for the banking industry’s continual improvement. To validate the properties of the methodology, we developed a case study involving 25 Colombian banks. Using Six Sigma metrics, DEA models, and slacks analysis, the results provide a comprehensive study of the quality performance, identifying each bank’s relative strengths and weaknesses in several quality dimensions. The data indicate that some banks perform better on quality characteristics such as customer happiness, dependable service, and procedures. However, this study also reveals a promising finding: banks still have the potential for development, particularly in their response time, channel efficiency, fraud, and credibility, offering hope for the future of banking services.

Regional efficiency analysis of fresh food cold chain logistics in China based on three-stage DEA model

PurposeAssessing the efficiency of fresh food cold chain logistics as accurately as possible is essential for industry development planning. This study was designed to analyze the efficiency of fresh food cold chain logistics in China.Design/methodology/approachA three-stage data envelopment analysis (DEA) model was used to analyze the efficiency of fresh food cold chain logistics in 30 provinces of China from 2013 to 2019. The stochastic frontier analysis (SFA) model in the second stage was used to eliminate the influence of external environmental factors and random disturbances on efficiency analysis results.Findings(1) The overall actual efficiency of fresh food cold chain logistics in China is unsatisfactory, with an average technical efficiency of 0.382 over the 7-year period. (2) The national average technical efficiency and average scale efficiency were overestimated by 29.9% and 40.0%, respectively, compared with the actual values. (3) The efficiency of fresh food cold chain logistics does not align with the level of regional economic development. (4) Distinct regional variations exist in the efficiency of fresh food cold chain logistics in China, with higher efficiencies observed in Northwest China and the Central Yangtze River regions, and the lowest efficiencies in the northeast regions.Originality/valueThis study applies a three-stage DEA model to assess the development and regional differences of fresh food cold chain logistics in China, enriching the application of models and empirical analysis in this field. By analyzing the situation in China, it provides ideas and references for other developing countries to develop cold chain logistics.

Regional Healthcare Resource Allocation and Decision-Making: Evaluating the Effectiveness of the Three-Stage Super-Efficiency DEA Model

This study addresses the challenge of achieving a more rational allocation of medical resources at the regional level, using Guangxi Province, China, as a case study. A three-stage super-efficiency Data Envelopment Analysis (DEA) model is employed to assess and analyze the effectiveness of resource allocation. The research methodology involves identifying input, output, and environmental variable indicators to construct a healthcare resource allocation index system. The indicator data are processed using Excel software. The three-stage super-efficiency DEA model is then applied to evaluate the healthcare system in Guangxi Province, focusing on Pure Technical Efficiency Change (PEC), Scale Efficiency Change (SEC), Efficiency Change (EC), Technological Change (TC), and Total Factor Productivity (TFP). Finally, the Malmquist index method is utilized to measure and dynamically analyze the efficiency of healthcare resource allocation. The study's results show that, from a static perspective, the average comprehensive efficiency is 1.067 before adjustment and 1.054 after adjustment, indicating relatively high overall efficiency in healthcare resource allocation in Guangxi Province. However, environmental factors and random errors have led to an overestimation of healthcare resource allocation efficiency, which the three-stage super-efficiency DEA model effectively corrects. Additionally, the average SEC and PEC values are 0.997 and 0.998, respectively, both below 1. This indicates that both scale efficiency and pure technical efficiency contribute to a decline in technical efficiency. Based on the results of the sensitivity analysis, the conclusions regarding the efficiency of healthcare resource allocation in Guangxi are deemed highly reliable. Despite the influence of uncertain factors, the model consistently provides stable and coherent assessment results in most scenarios. Therefore, special attention is needed to improve scale efficiency in healthcare resource allocation within the region, alongside enhancing management and technological capabilities in the healthcare sector. Overall, this study provides valuable reference and guidance for researchers and practitioners in related fields and offers scientific decision support for healthcare resource allocation.

Spatial-Temporal Evolution and Determinants of Green Economy Efficiency: An Integrated Analytical Approach

In response to global environmental challenges and the need for sustainable development, this study investigates the spatiotemporal evolution and determinants of green economy efficiency (GEE) across 30 Chinese provinces from 2005 to 2021. By employing a Slack-Based Measure (SBM) Data Envelopment Analysis (DEA) model, kernel density estimation, and spatial Markov chain analysis, this study provides a comprehensive assessment of GEE, incorporating both spatial and temporal dimensions. The analysis reveals a positive overall trend in GEE, with notable regional disparities, as eastern provinces exhibit significantly higher efficiency levels compared to central and western regions. Additionally, GEE improvements have been observed nationwide, although the central and western regions lag. Key factors influencing GEE include energy intensity, economic development, urbanization, industrialization, transport infrastructure, and government intervention, with distinct regional variations in their impact. The findings suggest that optimizing economic growth, reducing energy intensity, and fostering green urbanization and industrialization are essential strategies for enhancing GEE. This research contributes to the theoretical understanding of green economy efficiency and offers valuable policy recommendations for achieving balanced regional development and sustainable growth in China.

A Novel Centralized Allocation Data Envelopment Analysis Model for Carbon Emission Allocation Under a Heterogeneous Abatement Cost: Application Within the Chinese Industrial Sector

This paper presents a mathematical approach to analyzing carbon abatement costs and the allocation of carbon emission allowances in China’s industrial sectors. We utilize input–output data from 30 Chinese provinces between 2009 and 2018 to estimate carbon abatement costs by applying the slack-based measure (SBM) efficiency model and its dual form. The SBM model captures inefficiencies and offers a rigorous framework for measuring abatement costs. Using these costs, we develop a centralized allocation data envelopment analysis (DEA) model, which maximizes sectoral benefits through optimal reallocation. This DEA model is formalized as a linear programming problem, with the aim of determining the efficient allocations of carbon allowances while maintaining the system’s economic productivity. Furthermore, we construct intertemporal, interregional, and spatiotemporal allocation DEA models to examine the dynamics of carbon emission allowance allocation over time, space, and combined spatiotemporal dimensions. These models offer insights into the efficiency of carbon markets under varying conditions. Our proposed new mathematical formulations reveal optimal allocation strategies that can balance emission reductions with industrial productivity. This study also provides novel mathematical frameworks for analyzing the carbon allowance distribution and contributions to both the theory and application of mathematical optimization in environmental policy design. Our findings reveal that China’s industrial carbon abatement costs exhibit significant interprovincial and regional differences. Developed provinces with higher levels of industrial development have higher carbon abatement costs, while provinces with less-developed industrial sectors have lower costs. Under the interregional allocation scenario of carbon emission allowances that consider abatement costs, developed provinces have smaller industrial carbon emission reductions, whereas less-developed provinces have larger reductions. In the intertemporal allocation scenario, provinces with larger industrial economies face greater emission reduction tasks. Under the combined interregional and intertemporal allocation scenario, industrial sectors in coastal developed provinces have lower carbon emission reductions, while those in inland less-developed provinces have higher reductions, mirroring the spatial allocation results of carbon emission allowances.

Banks’ financial performance analysis: an experience from an Islamic economy

Purpose This study aims to analyze the performance efficiency of private commercial banks in Iran as an Islamic economy. Design/methodology/approach A meticulously chosen sample comprising 15 private banks from 2020 to 2023 has been examined. This study uses data envelopment analysis (DEA) models, specifically the Constant Returns to Scale (CCR) with input orientation and the Variable Returns to Scale (BCC) with input orientation, to scrutinize performance efficiency relative to the banking sector’s average efficiency ratio. Findings The findings indicate that the performance of decision making units is superior in BCC models when determined with CCR models. The CCR-I analysis spanning 2020–2023 reveals that only two banks consistently demonstrated full efficiency performance, attaining a 100% efficiency score across all years. The observed fluctuations in banks’ efficiency performance are attributed to disparities between the growth or reduction in inputs and the corresponding augmentation or diminution in outputs. Research limitations/implications This paper has some limitations. First, selecting inputs and outputs for the DEA models is crucial, as this study’s results are sensitive to these choices. Second, this research focused exclusively on the efficiency of private banks and did not assess the efficiency of governmental banks. Practical implications The insights from this study can be valuable for bank managers in evaluating performance, regulators in maintaining financial stability and investors and market analysts in enhancing their investment strategies through bank performance analysis. Originality/value To the best of the authors’ knowledge, this study is the first to propose a framework for evaluating and forecasting commercial bank performance in an Islamic background. This model for prediction performance would be helpful for future research on the banking sector. In addition to improving Islamic banking literature, this research contributes to the literature regarding the analysis methods usually used.

Analysis and forecasting of the importance of smart contracts and blockchain technology in SCM in Vietnam by using DEA and Grey model

In the context of a complex and demanding business environment, blockchain and smart contracts are becoming increasingly important in supply chain management in Vietnam. The application of these technologies can help to optimize processes, create a reliable platform for tracking, managing, and forecasting supply chains, and enhance transparency and integrity. In the study of supply chain management in Vietnam, the methods applied are DEA (Data Envelopment Analysis) and the Grey forecasting model. These two methods not only focus on optimizing current processes but also create opportunities for analyzing, evaluating, and forecasting the importance of blockchain technology and smart contracts. This research topic not only raises questions about the potential for optimizing current processes, but also proposes innovative solutions to enhance transparency, integrity, and responsiveness to volatile markets. Additionally, the research aims to propose appropriate strategies for applying these new technologies to business practices in Vietnam, creating a flexible, secure, and adaptable supply chain system that is responsive to the changing business environment of the present.

Research on the contribution of technological innovation efficiency and internal structure optimization of charging pile industry: A case study of China

In the context of resource scarcity and environmental protection, the new energy industry has garnered significant attention from various sectors. The charging pile (CP) industry, a crucial component of the new energy vehicle (NEV) industry's supply chain, requires improvements in both quantity and quality. This study examined the technological innovation efficiency (TIE) of the CP industry, considering two levels: pure technical efficiency (PTE) and scale efficiency (SE), and explored methods to optimize its internal structure. By combining the grey correlation analysis (GCA) method with the generalized data envelopment analysis (GDEA) model, a reliable evaluation indicator system was established. Empirical findings revealed that the PTE of technological innovation in the CP industry exhibited an upward trend from 2019 to 2022, achieving a GDEA-effective state. However, the SE of technological innovation shows a downward trend, indicating non-GDEA effectiveness. The contribution of CP companies to the PTE of technological innovation has shown a persistent and accelerating decline, while their contribution to SE, once negative, gradually shifts toward a positive influence. Consequently, the internal technological innovation structure of the CP industry necessitates optimization and adjustment to attain balance. This study provides recommendations from the perspectives of both the government and companies.

Measurement and spatiotemporal differentiation of green water resources utilization efficiency in the Yellow River Basin

ABSTRACT To further explore the green water resources utilization efficiency (WRUE) in this basin, a corresponding method was designed. Firstly, a measurement index system for WRUE was studied and constructed. With this indicator system, the study introduced a data envelopment analysis model to analyze it, and then combined the relaxation variable analysis strategy to comprehensively analyze the accuracy of WRUE. The experiment outcomes indicated that in the WRUE of the Yellow River Basin (YRB), the pure technical UE changed the most and was in an effective utilization state. However, overall, the average values of scale efficiency and green UE within the basin were both below 1, indicating that it has not been fully utilized effectively. Besides, the average utilization efficiency of water resources in the upper, middle, and lower reaches was 0.51, 0.63, and 0.85, respectively. The analysis results prove that the measurement method designed in the research can effectively analyze the WRUE of the YRB, provide effective support for the efficient and circular development of the regional economy, and promote regional economic development.

The Use of the Data Envelopment Analysis–Malmquist Approach to Measure the Performance of Digital Transformation in EU Countries

Currently, the process of the digital transformation of EU countries is very important and often discussed. It will not only bring new opportunities for companies and the broader population but will also enable the transition to a more ecological economy. An important goal is to speed up the digitalization processes taking place in companies. It is very important to use already established digitalization elements more efficiently. This also resulted in the motivation for the given research. The aim of this paper is to quantify the change in the efficiency of the digital transformation of EU countries. As part of this research, the Variable Returns to Scale Data Envelopment Analysis (VRS DEA) model and the Malmquist index (MI) based on the DEA approach were applied. The results of the model made it possible to assess how the changes in technical efficiency and technological changes contributed to the changes in efficiency. The long-term theoretical added value of this paper lies in its proposal for countries and their governments to monitor not only the number of introduced digital elements, but also the efficiency of their use relative to some aggregate output; for example, GDP (Gross Domestic Product) or unemployment rate. The added value of this research is that less developed countries use digitalization elements more effectively than developed countries.

Energy efficiency in middle-income countries with DEA: An approach for Latin America

Latin America and Caribbean countries are focused on meeting their 2030 sustainable development goals, with the promotion of actions that increase energy efficiency being a key pillar. Recent studies have applied various data envelopment analysis (DEA) models to this end, but few include Latin American countries. This study assesses energy efficiency in 18 middle-income Latin American countries from 2008 to 2019 using two super-efficient DEA radial models that are particularly adept at distinguishing between highly efficient units. The analysis incorporates a broader set of countries through clustering techniques to ensure homogeneity. From this analysis, the production function, the technical efficiency, the total energy efficiency factor and their respective correlation coefficients versus the per capita income variable are obtained for each country. Costa Rica and Argentina stand out among the Latin American middle-income countries with the highest energy efficiency within quintile Q1 and Q2, whereas Paraguay and Chile obtain the lowest efficiency within quintile Q5 and Q3. For the 18 countries studied, a higher level of income is not significantly related to a higher level of energy efficiency. This study provides policymakers an alternative indicator for effective monitoring of energy efficiency.

Pitfalls in the assessment of higher education learning gains in Brazil through multilevel regression: handling the ceiling effect.

This work assesses the validity of the Brazilian Indicator of the Difference between Observed and Expected Performances (IDD), which applies a multilevel regression model to rank university courses. It proposes a methodology to test for inconsistencies and research for the causes. With this objective, we develop an experimental methodology, applied to a database with 4,432 production engineering students distributed over 329 undergraduate courses, from the government’s National Institute for Educational Studies and Research. We display a graphical analysis of the expected and achieved grades in final graduation exams, the gap in which determines the learning gains for every course. Then we apply the multilevel linear regression model of the IDD in a particular scenario, to test for consistency in value-added learning measurements using the technique reductio ad absurdum. The result reveals that the present methodology for the calculation of the IDD is inadequate and inconsistent, and that this can be explained by the ceiling effect. It needs to be radically revised to prevent unfair judgements. Finally, we propose and implement a production frontier technique to correct for the ensuing ceiling effect, namely a variable returns to scale data envelopment analysis model for adjusting the measurements using evidence based benchmarks.

Quantitative evidence of the community of shared future for mankind as a driver of sustainable development in human society

In an era marked by global economic challenges, an increasing array of non-traditional security threats, and a proliferation of conflicts and issues, China has put forth the building of a community of shared future for mankind as a way to address this question of the times. The theory of the community of shared future for mankind offers a framework to address contemporary issues by promoting equality, mutual benefit, security, justice, openness, innovation, multicultural exchange, and green development. This paper innovatively quantifies the five core principles of the theory of the community of shared future for mankind using the entropy method. It calculates the proxy variable for sustainable development, green total factor productivity, using the super-efficiency Slack-Based Measure model in Data Envelopment Analysis. Baseline regression is conducted on this basis. The Principal Component Analysis method and another Data Envelopment Analysis method, the Malmquist index, are utilized to recalculate the indicators, followed by another round of regression analysis. The results are consistent with the baseline regression. Additionally, Ordinary Least Squares, Partial Least Squares, Maximum Likelihood Estimation, Ridge regression, and Lasso regression are employed to ensure the robustness of the results. The findings indicate a significant positive relationship between the community of a shared future for mankind and the sustainable development of human society, with consistent conclusions drawn from different calculation methods and regression techniques. Through value recognition, international cooperation based on benefit-sharing and mutual reciprocity can guide humanity towards a harmonious coexistence with nature, irrespective of regional and economic disparities. This study provides quantitative evidence of the impact of the community of a shared future for mankind on the sustainable development of human society, filling a gap in existing research. Based on these findings, feasible policy recommendations are proposed to promote sustainable development in human society.