Regional Ecological Efficiency 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.

Can the digitalization level of agriculture improve its ecological efficiency under carbon constraints: Evidence from China

The interplay between digitalization and economic development constitutes a pivotal global issue, yet empirical research on agricultural ecological efficiency in developing countries remains limited. This study initially establishes a measurement system and a comprehensive index for the level of agricultural digitalization. Subsequently, it delineates the relationship between agricultural digitalization level and agroecological efficiency using the spatial Durbin model, and ultimately explores the enhancing effect of agricultural digitalization level on agroecological efficiency using China as a case study. Research reveals that the agricultural ecological efficiency across the 31 mainland Chinese provinces demonstrates a generally linear upward trajectory, embodying both agglomeration and heterogeneity. The level of agricultural digitization exerts a significant, positive direct impact and facilitates a spatial spillover effect on agricultural ecological efficiency. Other control variables, such as financial support for agriculture and local economic development, impart a positive direct impact on regional agricultural ecological efficiency, while rural household operating income propels a positive spatial spillover effect on adjacent areas. The findings furnish guidance for developing countries to adeptly execute digital rural construction, aiming to enhance agricultural ecological efficiency amidst carbon constraints.

Evaluation of China’s provincial eco-efficiency with the explainable boosting machine (EBM) model and Tobit regression

The explainable boosting machine (EBM) model measures China’s provincial eco-efficiency, and the Tobit regression model reveals internal driving factors to provide consultation for promoting China’s green development based on the panel data of 30 provinces from 2000 to 2018. The findings of this article are as follows: provincial-level regional ecological efficiency is low, growth is slow, regional differentiation is significant, and development still has a trend of incoordination and multi-polarization. From the perspective of global autocorrelation, the Moran index is significantly positive, and the province eco-efficiency of the first grade presents a positive spatial correlation and has the characteristics of spatial agglomeration. The regression analysis results show that the economic level, the FDI, the level of technological innovation, and the level of human capital are the main influencing factors of eco-efficiency, and there are spatial differences. Relevant suggestions are based on the status and influencing factors of the unbalanced development heterogeneity of provincial eco-efficiency in China.

Dynamic Evolution and Convergence Analysis of the Ecological Efficiency of China’s Fisheries

The dynamic evolution and regional heterogeneity of fishery efficiency development must be explored from an ecological perspective to reveal the spatial pattern of fishery ecological efficiency. Thus, taking 30 provinces/cities in China between 2006 and 2020 as research objects, we measured the level of the fishery ecological efficiency and regional fishery ecological efficiency convergence indexes using the superefficiency and convergence indexes, respectively. We found the following: (1) The ecological efficiency of China’s fishery shows a wavelike upward trend with significant fluctuations, but the overall efficiency level is not high. (2) The development of regional fishery ecological efficiency is not well coordinated; however, the variation curve of fishery ecological efficiency in the eastern and central regions is gradually decreasing. (3) A trend of “catching up and surpassing” is observed in the development of fishery ecological efficiency in various regions of China, and regional fishery ecological efficiency gradually converges to the regions’ respective steady-state levels over time. The results indicate a significant gap in regional fishery ecological efficiency, as well as polarization; areas with lower fishery efficiency are catching up with high-efficiency areas at different speeds, and regional fishery ecological efficiency is stabilizing.

Exploring the Role of Foreign Direct Investment and Environmental Regulation in Regional Ecological Efficiency in the Context of Sustainable Development

Eco-efficiency (EE) is an important indicator of regional sustainable development, which reflects the efficiency of regional economic development in using resources effectively to reduce environmental pressures, and foreign direct investment and environmental regulation are very important in promoting regional economic growth and enhancing eco-efficiency. In this paper, we chose China’s panel data from 2009 to 2021, measured China’s regional eco-efficiency using the super-efficient SBM model, and explored environmental regulation and the impact of FDI on EE in different regions using the Tobit model, with conclusions as follows: (1) The average value of national EE in China during 2009–2021 was about 0.631, which was at a low level, and there were significant differences between regions, with the highest EE in the eastern region, and the central and western regions being lower than the national average. (2) FDI at the national level had a significant promoting effect on regional EE, with an elasticity coefficient of 0.0213, which verifies that the “pollution paradise” effect does not exist at national level. FDI promoted EE in the eastern region, while not being significant in the other two regions. The impact of the environmental regulation act on EE at the national level did not pass the significance test, but the impact passed the significance test with positive coefficients for both the eastern and central regions, while in the western region it was not significant. (3) Financial investment in science and technology promoted EE in the national, east, and central regions significantly, while not being significant in the western region. The economic development level of all regions was positively correlated with EE; the impact of urbanization on EE was significantly positive in national, central, and western regions, but was not significant in the eastern region. The industrial structure of all regions was not conducive to the improvement of EE, with the western region having the most negative impact on EE. The study in this paper represents an important addition and refinement to research in related fields.

Can industrial collaborative agglomeration drive the regional ecological efficiency improvement to fulfill COP26 requirements?

COP26 provided a roadmap to world leaders for taking policy measure for the climate change effects mitigation. In this regard, policy makers of major countries devoted their overwhelming support. Similarly, to achieve the targets of COP26, the role of industrial and energy sector critically important. In this paper, we have developed a new energy saving path to meet COP26 requirements through the industrial collaborative agglomeration index (ICAI) model which is constructed by taking location entropy of single industrial agglomeration as the basis. The super undesirable SBM (SUSBM) model is constructed to measure regional ecological efficiency (EE). Results show great differences in ICAI among the 3 regions and 11 provinces. Industrial collaborative agglomeration level of the upstream region shows fluctuation upward trend, while that of midstream and downstream regions shows fluctuation downward trend. The EE in downstream region is highest. The impact of ICAI on EE is significant and shows as a U-shaped curve. The increasing of proportion of the secondary industry in the industrial structure and increasing of per capita energy consumption inhibit the improvement of EE. The high proportion of non-state-owned economy in the economic system, the strengthening of environmental regulation intensity, and the improvement of economic development level and push technological innovation are conducive to the improvement of regional EE.

Spatial spillover effects of national-level eco-industrial park establishment on regional ecological efficiency: evidence from 271 cities in China.

Using a panel dataset of 271 Chinese cities at the city level from 2004 to 2018, this study is the first to adopt a staggered spatial difference-in-differences (SDID) model to investigate the effect and mechanism of the national-level eco-industrial park (NEIP) policies on eco-efficiency. Moreover, this study deeply identifies the policy effect by using a spatial difference-in-difference-in-differences (SDDID) model, spatial attenuation boundary, and event study method. The results show that NEIP can significantly and consistently improve urban eco-efficiency. However, NEIP has a continuous negative effect on the eco-efficiency of the surrounding area. The siphoning effect of the NEIP on eco-efficiency is more pronounced in cities with more than one NEIP or in provincial capitals and municipalities with NEIPs. In addition, the spatial effect of the eco-efficiency of the NEIP can spread for approximately 100km; i.e., there is a negative impact on the cities in the immediate vicinity of the NEIP (within 100km). Moreover, the impact of the NEIP on urban and even regional eco-efficiency is mainly realized through the crowding out effect of heavily polluting enterprises and the technological innovation effect. Therefore, based on continuing to expand the NEIP pilot cities, the government should establish several regional eco-industrial city clusters centered on the pilot cities, adopt policies, and build the corresponding infrastructure. In addition, taking into account regional differences, the government should construct differentiated eco-industrial park goals and support policies to achieve regional economic development based on environmental protection.

Study on the effect of industrial structure transformation on regional ecological efficiency in ecological economic zone under carbon peaking and carbon neutrality strategy

As the main strategy for the coordinated development of China’s ecological economy in the future, the carbon peaking and carbon neutrality strategy is an important response strategy to solve the two major problems of China’s urbanization process: the industrial structure is biased toward tradition and the economic development is biased toward high carbon. Under this development prospect, how to coordinate ecological development and economic development through the transformation of the industrial structure and form a unified development situation is an important issue for the development of the ecological economy. To solve this problem, the research starts with the theory of industrial structure change and ecological efficiency, puts forward two kinds of ecological efficiency assumptions, and establishes a mathematical model based on the C-D production function and cost function for the impact of industrial structure change on ecological efficiency. On this basis, a spatial model is formed, and empirical analysis is carried out for the important domestic ecological economic zone - the Yangtze River Economic Belt. The research results show that the industrial structure of the eco-economic zone will have a significant positive impact on ecological efficiency. At the same time, under the effect of inter-city cooperation, ecological efficiency has a significant spatial spillover effect. At the same time, the spatial lag factor and spatial error factor of urban agglomeration in the Yangtze River Delta are significant at the 10% and 5% levels, and the spatial lag factor and spatial error factor of urban agglomeration in the middle and long reaches are significant at the 5% level. It can be seen that the spatial dependence and linkage of ecological effects are strong, and there is a relatively significant interaction.

Can Inbound Tourism Improve Regional Ecological Efficiency? An Empirical Analysis from China.

Inbound tourism has an important impact on regional eco-efficiency. This paper uses the panel data of 31 provincial administrative units in China from 2005 to 2019; uses the improved DEA model to measure the regional ecological efficiency; and uses the panel threshold model to investigate input, output, and efficiency from the perspective of green technology innovation. Then, it explores the heterogeneous effects of inbound tourism on ecological efficiency. This paper finds that cross-border tourism has a positive impact on the ecological efficiency of tourist destinations. However, the degree of influence varies and will be changed with the level of regional green innovation. The main conclusions are as follows: (1) From an overall perspective, inbound tourism has a significant positive effect on ecological efficiency. (2) With the increase in green innovation investment and output, the promotion effect of inbound tourism on regional ecological efficiency first increases and then decreases. (3) The higher the green innovation efficiency, the greater the promotion effect of inbound tourism on ecological efficiency. Therefore, the Chinese government should encourage the development of inbound tourism, adopt greener innovative technologies that are cleaner and more environmentally friendly, and enhance the welfare effect of tourism on green economy.

Digital Economy, Environmental Regulation, and Ecological Well-Being Performance: A Provincial Panel Data Analysis from China.

China is currently in a strategic opportunity period for green and high-quality development, and developing the digital economy is an important choice to achieve environmental pollution control, improve regional ecological efficiency, and enhance social welfare. In this context, the impact of the digital economy on ecological well-being performance and the role of environmental regulation need to be examined. In this study, the super-efficiency SBM-DEA model was used to measure the level of ecological well-being performance in 30 provinces of China from 2011 to 2019. On this basis, the mediating effect model and spatial Durbin model were adopted to explore the transmission mechanism and regional heterogeneity of the impact of the digital economy on ecological well-being performance. The empirical results show that the digital economy significantly contributes to regional ecological well-being performance in China, and there is significant spatial spillover as well. Moreover, the findings still hold under robustness tests. The results also show that environmental regulation is an important transmission path for the digital economy to enhance regional ecological well-being performance, and the impact of environmental regulation on ecological well-being performance varies by region; specifically, the impact in eastern China is positive but not significant. However, the digital economy plays a significant positive role in promoting ecological well-being performance in the central and western regions, and is more obvious in the central region. Finally, suggestions are put forward to enhance the role of the digital economy in regional ecological well-being performance, which is of great significance for promoting green economic growth and high-quality development.

A data-driven approach for the measurement and improvement of regional industrial ecological efficiency for carbon peaking and carbon neutralization.

Industrial ecological efficiency is regarded as an urgent challenge that affects the development of ecological civilization and environmental governance. Here, we propose a data-driven approach to measure and promote regional industrial ecological efficiency. We collected data related to regional industrial development and used the Data Envelopment Analysis-Banker Charnes and Cooper (DEA-BCC) model to measure regional industrial ecological efficiency from a static perspective. The Malmquist index model was then used to measure regional industrial ecological efficiency from a dynamic perspective. In addition, we used a Tobit regression model to identify the factors affecting regional industrial ecological efficiency. Through a case study of regional industrial ecological efficiency, we demonstrate the specific application of the proposed data-driven approach. This study provides a new and effective tool for improving industrial ecological efficiency at a regional scale. This method can help enterprises and local governments improve industrial ecological efficiency, coordinate the relationship between industrial economic growth and the ecological environment, and boost regional efforts to achieve carbon peaking and carbon neutralization goals.

Spatiotemporal Patterns and Influencing Factors of Industrial Ecological Efficiency in Northeast China

Scientific measurement of regional industrial ecological efficiency and discussion of the development and changes of its spatiotemporal pattern are of great significance to accelerate the high-quality development of regional economy and coordinate the development of industrial economy and ecological environment. Taking the old industrial bases in Northeast China as the research case and 2004–2019 as the research period, a super-slack-based model was used to measure the industrial ecological efficiency of 34 prefecture-level cities in the region. Meanwhile, the spatial autocorrelation model and the geographically and temporally weighted regression (GTWR) model were used to analyze the spatiotemporal pattern characteristics and the spatiotemporal heterogeneity of influencing factors. The results showed that: (1) From a time change perspective, the overall industrial ecological efficiency of Northeast China declined, the mean of the 34 cities decreased from 0.675 to 0.612, the number of cities with a high level of industrial ecological efficiency decreased significantly, the number of cities with a low level of industrial ecological efficiency increased significantly, and the development gap between cities within the region widened. (2) In terms of spatial pattern, the difference in the spatial pattern in the east–west direction decreased, and the spatial pattern in the south–north direction was enhanced. The industrial ecological efficiency of the central part of Northeast China gradually became the highest in the whole region. (3) From 2017, the industrial ecological efficiency had stable spatial autocorrelation characteristics. The local spatial autocorrelation was dominated by L-H-type cluster areas in the mountainous regions and L-L-type cluster areas in central and southern Liaoning province. H-H and H-L types had small numbers. In addition, the trend of H-H cities transforming into H-L cities was obvious, and the high level of urban space spillover effect showed good development. (4) The science and technology input, industrial agglomeration intensity, and environmental regulation of the government generally had a promoting effect on the improvement in industrial ecological efficiency, while the economic extroverted degree had a negative impact. The high-value area of science and technology investment and industrial agglomeration intensity concentrated significantly in the central part. The government focused on ecological protection areas and ecologically sensitive areas, and the economic extroverted degree had a significant positive impact on the two major urban agglomerations in central Northeast China. Therefore, differentiating measures should be taken according to the actual situation of each city to improve the industrial ecological efficiency level in Northeast China.

Does the legacy of state planning put pressure on ecological efficiency? Evidence from China

AbstractBuilding a green and healthy economic system and achieving high‐quality economic growth have long been the development goals pursued by the Chinese government. Under the local government competition system, coordinating the relationship between economic growth and ecological sustainability is a decisive factor for China's high‐quality development. As a former state planned economy, China inherited a legacy of setting economic growth targets at various levels of government. This paper analyzes the relationship between economic growth targets and ecological efficiency by using panel data models and spatial econometric techniques. Moreover, the mediating effect model and dynamic threshold effect model are used to study their influence mechanism and the possible nonlinear relationship. The results indicate that there is an inverted U‐shaped relationship between economic growth targets and ecological efficiency. Optimizing industrial structure and R&D investment can improve regional ecological efficiency. The economic growth targets in neighboring regions have a “U”‐shaped relationship with local ecological efficiency. The mechanism test results suggest that economic growth targets affect ecological efficiency by influencing financial development, foreign direct investment, infrastructure investment, environmental regulation, and technological innovation. The impact of economic growth targets on ecological efficiency shows a significant threshold effect when economic growth targets, industrial structure, R&D levels, financial development, foreign direct investment, infrastructure investment, and environmental regulation are at different threshold values. Therefore, to promote regional healthy competition and ecological efficiency, the government should formulate scientific and reasonable economic growth goals and improve the official evaluation system.

Research on the impact of green finance and abundance of natural resources on China's regional eco-efficiency

This article uses the Super-efficiency DEA mothod to calculate the regional ecological efficiency, and selects a Tobit model to verify the impact of green finance and natural resource abundance on Chinese regional ecological efficiency. Results as follows: (1) The ecological efficiency of the eastern region is highest, and western region is lowest. Most of the low-efficiency areas are concentrated in the west. Then the ecological efficiency of most areas in Chian has shown an increasing trend. (2) From the perspective of explanatory variables, there is a negative correlation between the overall national green finance and eco-efficiency, which is manifested in a non-linear “U”-shaped characteristic; green finance and eco-efficiency in the central and western regions have negative correlation and “U”-shaped characteristic of non-linear relationship, but the east of China does not passed the significance test. (3) There is an inverted U-shaped relationship between the country's overall resource abundance and ecological efficiency. And it is not statistically significant in the eastern region, but it shows a significant inverted U-shaped relationship in the central and west of China. (4) The impact of FDI on China's regional eco-efficiency is generally positive. FDI in the central and western regions has negative correlation with eco-efficiency, but FDI in the eastern region is positively related to eco-efficiency; there are all negatively correlated in whole countey and regions between industrial structure and eco-efficiency; the level of regional economic development is generally positively related to regional eco-efficiency, and in the eastern region's eco-efficiency is positive, and the impact on the other two regions is negative.

Evaluation of Regional Ecological Efficiency and Intelligent Decision Support for Sustainable Development Based on Environmental Big Data.

To promote urbanization in the next stage, it is of great significance to explore the ecological efficiency of green ecological regions and advance the sustainable development of a social economic system. However, spatial heterogeneity has not been fully considered in the existing evaluation models or methods for regional ecological efficiency (REE), and the corresponding decisions on sustainable development are not the optimal solutions. To solve the problems, this paper explores the evaluation of REE and intelligent decision support for sustainable development by analyzing environmental big data. Firstly, the spatiotemporal evolution of REE was examined based on environmental big data to clarify the spatial layout of REE and the sources of the spatial differences. Next, a multiobjective optimal decision-making model was established for the sustainable development of a regional ecosystem, and the solving method was presented for the model. The proposed model was proved valid through experiments.

Spatial Interaction Spillover Effects between Digital Financial Technology and Urban Ecological Efficiency in China: An Empirical Study Based on Spatial Simultaneous Equations.

As a core component of the digital economy, digital financial technology has a complex interactive and interdependent relationship with ecological efficiency. From the holistic spatial interaction perspective, this paper uses spatial simultaneous equations and generalized spatial three-stage least squares (GS3SLS) to analyze the spatial interaction spillovers between digital financial technology and urban ecological efficiency based on data from 284 Cities in China from 2008 to 2018. The results show that: (1) Digital financial technology and urban ecological efficiency promote each other, and the latter is relatively dominant. (2) Both digital financial technology and urban ecological efficiency have significant spatial spillover effects. (3) Digital financial technology in surrounding cities has a restraining effect on local ecological efficiency, and the improvement of ecological efficiency in surrounding cities has a siphon effect on local digital financial technology. (4) There is spatial and period heterogeneity in the intensity of the spatial interaction spillover effect between the two. With resources and environment increasingly becoming rigid constraints on economic growth, these findings help identify new drivers of regional ecological efficiency and promote the coordinated development of digital finance and green ecology.

Impact of environmental regulation policy on ecological efficiency in four major urban agglomerations in eastern China

Rapid economic development of any country will usually lead to negative environmental impacts. A free market economy cannot fundamentally solve this issue, which requires the guidance and control of the government. The environmental policies of governments can effectively improve the ecological conditions in a region. This study quantifies environmental regulation policies and takes four urban agglomerations in eastern China as the research object to explore the influence of environmental regulation on regional ecological efficiency. First, policies can be divided into policy control, pollution control, ecological protection, and social adjustment by building a Latent Dirichlet Allocation (LDA) model of a policy text library to measure differences in urban policy bias. Next, a slack-based measure (SBM) model was used to measure urban ecological efficiency. Finally, using the qualitative comparison analysis method, the time effect was considered and the ecological efficiency configuration of the entire region was obtained. In addition, contrast analysis was conducted for different path configurations and the reasons of the statuses of the four urban agglomerations were obtained. The results show that policy control, pollution control, ecological protection, and other mandatory policies can significantly reduce the negative environmental effects, especially when the policy controls involve an explicit form of punishment, which is a necessary condition for achieving a high level of ecological efficiency. However, social adjustment means having higher requirements for regional total factor development, which requires improving the environmental awareness of residents and improving corporate social responsibility. Regional differences will mean that areas with a lower level of economic development have a higher intensity of policy control, and the means of policy control should be matched with the level of economic development. In addition, this study proposes some policy suggestions, such as strengthening policy control and pollution prevention, strengthening social regulation, and promulgating environmental laws and regulations according to local conditions.

The spatial network and its driving factors for sustainable total-factor ecology efficiency: the case of China.

The interaction of regional ecological efficiency is important for promoting ecological efficiency. Using a gravity model and social network analysis, this study investigated the spatial network characteristics of the sustainable total-factor ecology efficiency (STFEcE) in 30 provinces of China from 2005 to 2016 for the first time. The quadratic assignment procedure (QAP) was also used to analyze the factors affecting the network. The results are as follows. (1) The STFEcE between regions exhibited a spatial network relationship. (2) Jiangsu, Guangdong, Shandong, Ningxia, and other provinces were in the center of the network, whereas Guangxi, Anhui, and other provinces were on the edge. (3) The 30 provinces were divided into four plates, and the connections in the network were primarily based on the relationship between plates. (4) The difference between urban population, energy structure, and technical advancement negatively impacted the network relationship. The provinces should fully understand the value of the STFEcE network and implement appropriate measures to achieve collaborative improvement of regional ecological efficiency according to their roles in the network.

Research on evaluation and influencing factors of regional ecological efficiency from the perspective of carbon neutrality

Carbon neutrality is an important environmental management tool to deal with the global greenhouse effect. Addressing climate change is an important starting point for promoting the high-quality development of China's economy and the construction of ecological civilization, as well as an important area for participating in global governance and adhering to multilateralism. In this paper, from the perspective of carbon neutrality, taking China's Jiangsu Province as an example, the super-efficiency DEA model is selected to measure the level of ecological efficiency in Jiangsu, and on this basis, the Tobit model is used to verify the main factors affecting regional ecological efficiency. The results are as follows: (1) The regional eco-efficiency in Jiangsu shows a trend of decreasing from south to north, with obvious phenomenon of “club convergence”, with significant spatial correlation and agglomeration. (2) The industrial structure is negatively related to the regional eco-efficiency of Jiangsu regions; Energy consumption structure has a positively impact in southern Jiangsu,but negatively in central and northern Jiangsu; FDI has a positively impact in central and northern Jiangsu, but negatively in southern Jiangsu; Regional economic development has a positively impact in southern and northern Jiangsu, but negatively in central Jiangsu; The progress in the technological level has positive effects on the eco-efficiency of all regions of Jiangsu. The final paper puts forward some suggestions based on the carbon neutral target Provide theoretical reference for achieving the goal of carbon neutrality.

Impact of technological innovation and industrial-structure upgrades on ecological efficiency in China in terms of spatial spillover and the threshold effect.

In the past 40 years, China's rapid industrialization has resulted in remarkable social progress and regional economic prosperity but also has caused problems, such as excessive resource consumption and environmental pollution. Ecological efficiency is an important indicator of whether economic efficiency and environmental efficiency are balanced. Exploring ecological efficiency is vital for achieving sustainable development, as technological innovation, industrial structure upgrading, and ecological efficiency are probably related. However, there has been little research on the relationships among them. In this study, we used the super-efficiency slacks-based model (SBM) involving undesirable output to calculate the ecological efficiency of 30 provinces in China from 2008 to 2017. We found that China's ecological efficiency has risen, fallen, then fluctuated, and that there were significant spatial differences. Then, we used 3 spatial econometric models for comparative analysis, which revealed that the independent effects of technological innovation on ecological efficiency was significant and negative; however, technological innovation demonstrated a strong positive effect in tandem with industrial structure upgrading, and industrial structure always had a positive effect on ecological efficiency. On this basis, we added the cross-item of technological innovation, and industrial-structure upgrades had a significant and positive impact on ecological efficiency, which was significantly greater than the independent effect of technological innovation and industrial-structure upgrades. To explore the mechanism of industrial-structure upgrading and technological innovation on regional ecological efficiency, we set the first 2 as threshold variables. We found that there is in fact a threshold effect of technological innovation and industrial-structure upgrading on ecological efficiency due to the mismatch of periods. These results provide a theoretical rationale for China to formulate strategies to improve ecological efficiency. Integr Environ Assess Manag 2021;17:852-865. © 2020 SETAC.