DEA Model Research Articles

Can carbon emission trading improve regional eco-efficiency? Based on the environmental innovation perspective.

This paper uses green innovation and environmental pollution as the mediating variables to construct a mediating effect model to investigate whether China's carbon emission trading policy can improve regional eco-efficiency by reducing regional environmental pollution and stimulating green innovation. This study is based on panel data from 30 provinces and municipalities directly under China's central government and autonomous regions from 2003 to 2019. The eco-efficiency of these provinces is measured using the super-efficiency DEA model and the difference-in-difference method (DID). The results show that (1) China's emission trading policy significantly improves regional ecological efficiency and the per capita GDP. The improvement can effectively improve regional ecological efficiency. (2) The mediating effect of green innovation and environmental pollution is significant. That is, China's carbon emission trading policy further improves regional ecological efficiency by stimulating more green innovation and reducing the synergy brought by environmental pollution. (3) There are differences in the feedback of this impact mechanism between different regions: It shows the characteristics of the western region being more significant than the eastern region. The central region has no significant effect. The research conclusion can provide a policy reference for the subsequent unified promotion of the construction of a carbon emission trading market nationwide and a theoretical basis for helping to achieve the "dual carbon" goal.

Research on total factor energy efficiency in western China based on the three-stage DEA-Tobit model.

As an essential material basis and power source for economic and social development, Western China's low energy use efficiency has hindered its sustainable economic development. This study aims to evaluate the total factor energy efficiency of the region and identify its influencing factors. A three-stage DEA model was used to measure the efficiency of 11 provinces from 2006 to 2021, and the Tobit model was employed to investigate internal factors. The findings show that (i) external environmental factors and stochastic perturbations have a significant impact on TFEE in the western region, overestimating integrated efficiency and scale efficiency and underestimating pure technical efficiency. (ii) the study of external influencing factors finds that the level of economic development increases input redundancy; the industrial structure increases capital input and labor input redundancy while decreasing energy input redundancy; and the energy consumption structure increases capital input and energy input redundancy while decreasing labor input redundancy. (iii) the study of internal influencing factors finds that the level of scientific and technological innovation, the level of openness to the outside world, and the TFEE have a positive correlation. In contrast, the intensity of environmental regulation has a negative correlation.

Forestry Subsidies, Forestry Regulatory Policies, and Total Factor Productivity in Forestry—Plot-Scale Micro-Survey Data from A Heterogeneous Forest Types Perspective

Enhancing the total factor productivity in forestry is an important part of deepening the reform of the collective forest rights system. Based on the survey data of 295 forest plots in 12 towns of Liuyang City, Hunan Province, China, the study utilized a three-stage DEA model to assess the total factor productivity of forestry at the plot level. The empirical study employs Tobit and fractional regression models to investigate the effects and differences of forestry subsidies and forestry regulatory policies on the heterogeneous total factor productivity of different types of forests. The study found that: (1) the mean value of plot-scale forestry total factor productivity is 0.127, and there are obvious differences in total factor productivity among timber forests, economic forests, and mixed forests; and (2) afforestation subsidies and nurturing subsidies significantly positively influence high-level TFP. Ecological benefit compensation positively affects high-level TFP, but is not significant at any level of TFP. Forestry regulatory policies negatively impact high-level TFP, but are not significant at any level of TFP. This paper puts forward countermeasure suggestions to improve forestry subsidy policies, optimize forestry regulatory policies, and improve forestry total factor productivity from the perspective of heterogeneous forest types.

Performance Test, Index System Establishment, and Comprehensive Evaluation of Earthquake Rescue Robots

To effectively enhance the adaptability of earthquake rescue robots in dynamic environments and complex tasks, there is an urgent need for a comprehensive evaluation method that encompasses establishing an evaluation index system, testing performance indexes, and conducting performance evaluation. Firstly, four main criterion and twenty-three sub-criterion indexes are established by conducting a comprehensive review of existing assessment measures for rescue robots across diverse domains. These indexes are validated through test modules developed by the National Earthquake Response Support Service to obtain corresponding values for each criterion. Moreover, a method for establishing the index system is proposed based on the fuzzy clustering analysis and grey correlation analysis methods. This method effectively addresses issues related to excessive subjectivity, redundancy, and ambiguous stratification of indexes. Subsequently, the DEMATEL is employed to scrutinize the interrelationships and causal connections among each index within the established index system, leading to the identification of input and output indexes based on the analysis outcomes. Finally, as an empirical example, three earthquake rescue robots are comprehensively evaluated and ranked using the super efficiency DEA model. Alongside analyzing results regarding input redundancy and output deficiency, targeted improvement suggestions are provided for each earthquake rescue robot. Additionally, comparison analysis with the entropy weight method and VIKOR method verifies the effectiveness of our proposed method.

Maximum consensus model with individual tolerance and mixed DEA prospect cross-efficiency for multi-attribute group decision-making

Experts are mostly not completely rational, and they generally exhibit different psychological attitudes and behaviors in multi-attribute group decision-making (MAGDM), which may lead to actual decisions deviating from the results obtained by traditional objective methods. Therefore, it is reasonable and necessary to capture individual tolerance and risk attitudes of experts when dealing with MAGDM problems. To achieve this intention, we design a MAGDM method that combines the maximum consensus model with individual tolerance and mixed DEA prospect cross-efficiency. In the consensus reaching process (CRP), we devise a maximum consensus model with individual tolerance, which can achieve a high consensus level with limited expert tolerance. In the selection process, we first define the prospect aggressive and benevolent cross-efficiency functions. Then, a mixed DEA prospect cross-efficiency ranking method is proposed, which reflects the attitudes of DMs (decision-makers) towards losses and gains. Moreover, this method avoids DMs’ hesitation and trouble in choosing between aggressive and benevolent DEA models. Subsequently, the algorithm and framework for the MAGDM method based on individual tolerance and risk attitude are proposed. Finally, the applicability of our method can be validated by an illustrative example. Sensitivity analysis and comparative analysis are supplied to indicate the rationality and superiority of our developed method.

Effectiveness of Higher Education Financing: DEA and SFA Modelling

The article aims to assess the effectiveness of financial expenditures in higher education for 31 provinces in China, namely overall, technical and scale efficiency, and to determine whether the returns to scale are increasing or decreasing for each province. The main method used is the three-stage DEA analysis / Data Envelopment Analysis, and software DEAP2.1 and Frontier 4.1 are used for modelling. Financial expenditures on higher education form an input parameter in the DEA model. The output parameters (determining the efficiency of higher education) are the number of students, the number of full-time teachers and the new value added to fixed assets in higher education institutions. The modelling was carried out for 2008-2013 and 2014-2018 (the COVID-19 pandemic is not considered to avoid uncommon trends and unpredictable disturbances in the model). A comparison of the analysis results for these periods allowed the identification of new trends and patterns. The study takes into account three main groups of parameters: 1) endogenous (inefficiency of internal management processes); 2) exogenous (gross regional product per capita of each province in China; the extent of university clustering, which is determined by political and market decisions and does not depend on educational institutions; and the degree of fiscal decentralisation, which is estimated in the article as the ratio of local and state budget expenditures per capita, taking into account the distribution of total budget expenditures for each district and the total population of the respective territory); 3) random disturbances. At the first stage of the study, a BCC model focused on inputs (to assess the technical efficiency of financial expenditures on higher education in different provinces) was built. It does not consider exogenous factors and random disturbances. In the second stage, SFA (stochastic frontier analysis) was applied to account for the impact of all three groups of factors (endogenous, exogenous and statistical noise) on the efficiency of financial expenditures in higher education for each province in China. In the third stage, the adjusted and standardised input and output parameters are incorporated into the BCC models, and the actual value of efficiency caused by exogenous parameters and the influence of random factors is determined. A comparison of the results of each modelling stage for each stage in the study and each province in China allowed the establishment of how the influence of the determinants of higher education financing efficiency changed in chronological and spatial contexts.

Changes in efficiency and physical size of container ports: An integration of genetic matching and stochastic data envelopment analysis

Benchmarking container ports of different physical sizes and figuring out the relationship between size and efficiency is complex due to the heterogeneous environment. In dealing with heterogeneity, the selection bias is often overlooked. Therefore, this study proposes an integrated multivariate genetic matching and stochastic DEA algorithm to evaluate the efficiency of container ports. It paves the way for container ports that differ in a selected feature, such as size, to be benchmarked using well-balanced clusters. Thus, the port managers can identify the most similar-featured peers in benchmarking with DEA or alternative models to acquire robust estimates without dependence on a longitudinal data set. The results of the model applied to international container ports imply the increase in size impacts efficiency negatively, while connectivity does positively, which contradicts the commonly held perception of stakeholders that the larger the container ports, the more efficient. That is, well-managed small container ports can also be as efficient. Therefore, it is concluded that the results of integrating genetic matching into the performance measurement provide beneficial inferences. In future research, clustering the observed container ports according to a specific feature and balancing clusters with multivariate genetic matching can provide valuable insights into the industry.

Determinants of Human Capital Development and Macroeconomic Freedoms: DEA Modelling

The purpose of the study is to identify countries with mechanisms to most effectively ensure that human capital losses do not become an obstacle to strengthening macroeconomic freedoms. The study is based on the hypothesis that a decrease in absence from work due to personal health problems, unemployment, and mortality leads to an increase in the level of six basic macroeconomic freedoms: business, labour, trade, investment, monetary, and financial freedoms. In this case, absence from work due to personal health problems, unemployment, and mortality serve as discouragers/inhibitors of human capital development, the growth of which reduces the chances of an employee obtaining the necessary knowledge and skills to effectively transform this employee’s labour into an economic asset. To generalise the scientific opinion on the studied issue, a bibliometric analysis of the relationship between each of these factors and the totality of all macroeconomic freedoms was carried out using the R Bibliometrix package in RStudio software. The basis of this analysis was formed by the databases of publications indexed by Scopus for 1937–2024, consisting of 1618, 1578, and 1517 literature sources for each factor-destimulator. This analysis allowed us to form clouds of the most used keywords and analyse the dynamics of their use, see how the relevant research areas and the keywords used have evolved, build a topic map of the research interface, etc. The information base of the study is the statistical data of Europa and the Heritage Foundation; the object is 30 European countries; the time horizon is 2021; the main method is the non-parametric method of Data Envelopment Analysis (CCR and BCC models); the software package is the rDEA and Benchmarking package in the R programming language. Frontier graphs were constructed that clearly show the efficiency frontier in the CCR and BCC orientations. Six iterations of DEA modelling were carried out, each using only one of the 6 indicators of macroeconomic freedom as an output and three indicators of human capital as inputs. The countries with the most effective national mechanisms to ensure that human capital losses do not become an obstacle to strengthening macroeconomic freedoms are Cyprus, Czech Republic, Iceland, Ireland, Malta, and Romania. These countries demonstrate “benchmark” efficiency in 6 cases out of 6 calculated. There is also a group of countries that have never been included in the list of “benchmark” countries: Austria, Belgium, Croatia, Estonia, France, Hungary, Greece, Italy, Lithuania, Latvia, Portugal, Serbia, Slovakia, and Spain. We have also identified countries that are “benchmark” not by all parameters, but only by certain ones: Bulgaria – only in terms of the impact of the studied human capital determinants on monetary freedom; Luxembourg – only on investment freedom and financial freedom; Poland – only on investment freedom.

The household resource efficiency and its economic determinants in China: A DEA and dynamic panel model

Identifying the level of household resource efficiency and its determinants is paramount, given its significant implications for resource governance and national-scale domestic resource planning. This study assesses household resource efficiency in China from 2016 to 2020, employing a super-SBM model alongside three waves of nationally representative longitudinal data. The investigation further delves into the intricate dynamics connecting household income, consumption propensity, and resource efficiency, utilizing maximum likelihood and structural equation modeling (ML-SEM) with dynamic panel data. Findings reveal an overall decrease in household resource efficiency levels, accompanied by diminished variation among surveyed households. Notably, a noteworthy non-monotonic relationship between household income and resource efficiency emerges, confirming a U-shaped pattern. Additionally, consumption propensity exhibits a negative influence, a trend that is further accentuated by increasing income, on household resource efficiency. These insights shed light on the complex interplay between household income, consumption, and resource efficiency, advocating for targeted policies to enhance resource management within households and on a national scale.

Spatiotemporal effect and influencing factors of the total factor energy efficiency: Evidence from the urban agglomerations in China

Amidst the ongoing global energy quandary, the imperative to augment energy efficiency emerges as a strategic response to counteract energy profligacy and propel sustainable development initiatives. This study aims to assist policymakers in gaining a more accurate understanding of the temporal and spatial complexity and dominant variables of total factor energy efficiency at the urban agglomeration level, thereby providing empirical insights for promoting a comprehensive improvement of the existing energy development framework. Based on the dynamic DEA model, this article utilizes an improved common frontier dynamic super-efficiency SBM model to conduct a quantitative analysis of total factor energy efficiency across 19 urban agglomerations in China. The influence factors of total factor energy efficiency are analyzed using spatial kernel density and geographical detectors at both temporal and factor levels. The research findings indicate that: (1) Total factor energy efficiency primarily fluctuates at a mid to low level, with a general decline to below 0.5 after 2018; (2) Significant regional heterogeneity is evident among Chinese urban agglomerations, displaying a “high in the northwest, low in the southeast” regional characteristic; (3) With time lag, the negative spatial spillover effect among regions further intensifies, becoming heterogeneous when the total factor energy efficiency of local urban agglomerations reaches 1.5 or above; (4) The singular and interactive effects of research funding become the main influencing factors of regional differentiation, and the integration of multiple factors is a key driver for enhancing total factor energy efficiency in the new era.

Digitalization and Efficiency of the Beninese Tax Administration

This paper aims to assess the effectiveness of the Beninese tax administration in the context of digitalization. Specifically, it seeks to determine the efficiency levels of various tax centers and evaluate the impact of digitalization on the efficiency of the Beninese tax administration. Using monthly panel data spanning from 2017 to 2023, a DEA model is employed to calculate efficiency scores. Subsequently, a censored Tobit model is utilized to examine the effect of digitalization on the efficiency of tax centers in Benin. The findings reveal average efficiency scores of 77.80%, 76.79%, 72.01%, 62.72%, 58.56%, 56.11%, and 46.16% for CIME Atlantique, CIME Ouémé Plateau, CIME Borgou Alibori, CIME Zou Collines, DGE, CIME Littoral1, and CIME Littoral 2, respectively. Moreover, digitalization, tax population, and additional taxes resulting from documentary audits positively influence the efficiency of tax centers. Conversely, the number of staff, the agent's directory, staff expenses, and the coverage rate of general accounting audits negatively affect the efficiency of tax centers. Decision-makers must prioritize enhancing the efficiency of tax administrations, expanding the tax base, and ensuring comprehensive tax control coverage to fully capitalize on the benefits of digitalization.

Research on the Transformation Efficiency of Scientific and Technological Achievements of Minis-try-affiliated Universities Based on Network DEA

Previous studies on the transformation of scientific and technological achievements in universities were based on the black box model, and the results obtained were not comprehensive enough. This paper divides the transformation of scientific and technological achievements of universities into the knowledge research and development stage and the value transformation stage. The two-stage network DEA model is used to study the transformation efficiency of scientific and technological achievements of 55 subordinate universities based on the data from 2009 to 2017.The research in this paper finds that the overall efficiency of the transformation of scientific and technological achievements in Chinese universities is relatively stable, and the efficiency of the knowledge research and development stage is relatively high, but the efficiency of the value realization stage is seriously low, resulting in a low level of overall efficiency."985" engineering colleges and "double first-class" colleges have higher input and output efficiency, and cluster analysis is used to classify and summarize the technological transformation of each college. Finally, corresponding development suggestions are put forward.

Spatial-temporal differentiation of urban eco-efficiency and its driving factors: A comparison of five major urban agglomerations in China.

This paper utilizes an improved undesirable output DEA model to measure the eco-efficiency of cities in five major urban agglomerations in China during 2006-2020. It employs the Theil Index and Geodetector to investigate the spatial-temporal distribution differentiation characteristics and driving factors of urban eco-efficiency. The main findings are as follows. Firstly, the eco-efficiency of all urban agglomerations showed a fluctuating upward trend, but the eco-efficiency performance of different urban agglomerations in China shows a stratification characteristic. Specifically, the Pearl River Delta urban agglomeration consistently ranks first in China, while the mean values of the Yangtze River Delta urban agglomeration, Beijing-Tianjin-Hebei urban agglomeration, and Chengdu-Chongqing urban agglomeration are lower than the national average. Secondly, the overall differences in the urban eco-efficiency of all sample cities show a consistently fluctuating downward trend. The factor that affects the level differences of eco-efficiency in different cities is the intra-regional differences. Last but not least, the top three factors affecting the spatial distribution difference of urban eco-efficiency in the whole sample are environmental pollution control investments, innovation level, and environmental infrastructure investments. In the end, this paper proposes that reducing the intra-regional differences is the primary task to achieve the coordinated improvement of urban eco-efficiency in urban agglomerations, and then puts forward some policy suggestions to improve eco-efficiency for the five major urban agglomerations.

Evaluation of Atmospheric Environmental Efficiency and Spatiotemporal Differences in the Yangtze River Delta Region of China

The scientific measurement of regional atmospheric environmental efficiency is an important prerequisite for achieving energy conservation and haze reduction and regional green and high-quality development. Taking the cities in the Yangtze River Delta region from 2012 to 2021 as the research object, the atmospheric environmental efficiency is measured from both static and dynamic perspectives using the three-stage DEA model and the Malmquist index to analyze the characteristics of spatial and temporal differences. The study finds that the real atmospheric environmental efficiency of the Yangtze River Delta region is 0.915, and the elimination of environmental factors and random errors is crucial to the assessment of the efficiency. The atmospheric environmental efficiency of the Yangtze River Delta region is not 1, and there is still room for improvement, in which the pure technical efficiency is the main factor that leads to the overall low efficiency. Different environmental variables have different impacts on the atmospheric environmental efficiency, in which the positive impact of the industrial structure is the most significant. Urban agglomerations can be categorized into “high–high–high”, “high–low–high”, “low–low–high”, and “low–high–low”. The total factor productivity of the atmospheric environment showed a gradual growth trend during the study period, in which technological progress played the most important role. Based on this, countermeasures are proposed to better enhance the level of atmospheric environment management in the Yangtze River Delta region.

Enhancing urban emergency management: A two-stage network DEA analysis of Chinese cities

In order to scientifically evaluate the emergency management capabilities of major cities in China, this article conducts a comprehensive study on the input and output indicators of emergency response in each city. A two-stage network DEA model was used to construct an evaluation model that reflects the emergency management capacity of cities. A dataset containing emergency management data from 36 well-known cities in China was selected to effectively evaluate its performance, and the city that demonstrated the most effective input-output ratio in the field of emergency management was ultimately determined. The research results show that using a two-stage network DEA model as the foundation to construct an evaluation model that reflects urban emergency management capabilities can promote a wise combination of subjective and objective evaluations, and achieve scientific investment in urban emergency assets.

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.

Efficiency Characteristics and Evolution Patterns of Urban Carbon Metabolism of Production-Living-Ecological Space in Beijing-Tianjin-Hebei Region

This study explored the carbon metabolism efficiency of a production-living-ecological space system, which is of great significance for regional factor integration and spatial optimization. In this study, the material flow analysis method was introduced to establish a framework for evaluating the carbon metabolism efficiency of the production-living-ecological space system, and the super-efficiency DEA model and Malmquist index were used to empirically analyze the spatio-temporal distribution, dynamic change, and evolution patterns of the carbon metabolism efficiency of production-living-ecological space in the Beijing-Tianjin-Hebei Region, China, from 2000 to 2020 on the basis of the urban metabolic perspective. The results showed that:① the carbon metabolism efficiency of the production-living-ecological space showed a fluctuating growth trend, indicating the significant spatial differentiation of carbon metabolism efficiency in each city. There was a low overall carbon metabolism efficiency level, with a distribution pattern of being high in the middle and low in the north and south. ② The Malmquist index showed that the Total Factor Productivity (TFP) of carbon metabolism efficiency was greater than 1, and both the Technical Change (TC) and Pure Efficiency Change (PEC) were less than 1, in which the TFP showed an increasing trend, whereas there was no significant contribution of technological progress or pure technical efficiency to carbon metabolism efficiency. The total factor productivity of more than 50% of the cities showed an improving trend, only 38.46% of which made technological progress in improving carbon metabolism efficiency, and more than half of the urban pure technical efficiency showed a decreasing trend, in which the technical efficiency change and scale efficiency change were greater than 1 in most cities. ③ There were different types of carbon efficiency characteristics in each city, and according to the movement rules of the corresponding points in the quartile map, the evolution patterns of tourism industry efficiency were classified into stable, reciprocating, progressive, and abrupt. Therefore, local governments should adopt differentiated strategies to reasonably allocate spatial resources of production-living-ecological space and improve the technical level and scale efficiency, so as to improve the efficiency of urban carbon metabolism.

Weighing in on the average weights: Measuring corporate social performance (CSP) score using DEA

This study develops a new DEA approach to incorporate such market-level information (i.e., average weights over firms) to create a composite corporate social performance (CSP) measure. The new model addresses the challenge that stakeholder expectations—expressed as the CSP weights of different CSP dimensions—are usually unknown at the firm level, but some reliable estimates of the average weights (across firms) are available at the industry or market level. Distinct from the traditional DEA, the new approach can incorporate constraints on the average values of weight distributions and is built on a computational framework new to the literature. The new model is compared with the traditional CSP approach, using longitudinal investment data by socially responsible funds (SRF). The regression results show evidence that the traditional CSP score, which is widely used in the management literature, may not be a reliable CSP score: firms with higher CSP scores tend to have lower SRF investment (in both the number of SRF and total shares). In contrast, the DEA-based CSP scores show the correct and positive correlation with SRF investment. Overall, this paper contributes to the literature by presenting a more reliable CSP approach and a new, generalizable DEA model. This is also the first empirical work documenting the potential validity issue of the traditional CSP scores.

Research on the environmental benefits of marine tidal energy and its impact on regional economic structure

Tidal power generation, being one of the earliest and largest developed new energy technologies, has evolved into a mature and increasingly competitive source of energy. This article delves into the evaluation of tidal energy's socioeconomic impact, establishing a connection between its environmental benefits and regional economic development. The focus is on marine economic efficiency, and the super-efficiency DEA model is employed to gauge this efficiency. The study conducts a thorough analysis of the green ocean economic efficiency in three major regions, introducing the Malmquist efficiency index for a comprehensive understanding of changing trends. Utilizing an economic effect model, the research explores the contribution of tidal energy development to regional economic growth, delving into industry-related effects. Through these analyses, we aim to provide a clearer and more comprehensive insight into the positive influence of tidal energy on regional socioeconomics. This understanding is pivotal for guiding sustainable tidal energy development and formulating relevant policies.

The Impact of Environmental Regulation on Collaborative Innovation Efficiency: Is the Porter Hypothesis Valid in Chengdu–Chongqing Urban Agglomeration?

Under the advocacy of sustainable and innovation-driven development, the potential impact of environmental regulation on collaborative innovation has become a controversial issue. This article uses panel data from 16 cities in the Chengdu–Chongqing urban agglomeration from 2011 to 2021 to analyze the impact of environmental regulation on collaborative innovation efficiency. First, this study uses the two-stage DEA model to analyze each city’s industry–university–research collaborative innovation efficiency. Then, the impact of environmental regulation on collaborative innovation is analyzed using the Tobit model. The results show that in the temporal dimension, the collaborative innovation efficiency of each city shows an upward trend. This demonstrates the outstanding effectiveness of transforming knowledge into technology for economic development. In the spatial dimension, the collaborative innovation efficiency of this urban agglomeration shows a “high in the center and low in the surroundings” pattern. The Tobit regression model shows that environmental regulation significantly impacts collaborative innovation in the Chengdu–Chongqing urban agglomeration. Command-and-control environmental regulation policies have a threshold effect on collaborative innovation, verifying the Porter hypothesis that appropriate environmental regulation promotes innovative activities. The results provide an initial basis for formulating regional environmental policies to achieve a win–win situation for innovation and sustainability in underdeveloped regions.