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As the cornerstone of the digital economy, the construction of digital infrastructure plays a crucial role in promoting China's high-quality economic growth.. Against the backdrop of the "dual-carbon" goals, the development of digital infrastructure will provide new momentum for carbon emissions reduction in urban areas. This study utilizes unbalanced panel data from 277 prefecture-level cities in China between 2008 and 2019, treating the smart city construction as a quasi-natural experiment, to systematically evaluate the impact of the pilot construction of smart city on urban carbon emissions intensity. The research findings reveal that the construction of the smart city has significantly contributed to the reduction of urban carbon emissions intensity, indicating that digital infrastructure contributes to urban carbon emission reduction. The reduction of carbon emissions resulting from smart city construction is particularly significant in the East and Central regions., as well as regions with high financial development levels, regions with high human capital levels and non resource-based cities. The construction of the smart city primarily achieves the reduction of urban carbon emissions intensity through two main pathways: improving the penetration rate of digital infrastructure and enhancing technological innovation capability. Therefore, this study recommends that local governments strengthen the integration and penetration of digital infrastructure with traditional industries, foster urban innovation vitality, and accelerate the transformation towards green and low-carbon cities.

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Utilizing city-level data from China, the paper employs a spatial econometric analysis to investigate the impact of fiscal decentralization on urban pollution. Empirical evidence indicates: (1) In the context of the emphasis of ecological civilization construction in China, an increase of fiscal autonomy for local governments is conducive to mitigating urban pollution intensity. Specifically, fiscal decentralization in one city not only promotes a reduction in local pollution intensity but alleviates environmental pollution problems in adjacent cities through spatial spillover effects. (2) Industrial structure upgrading and green technology progress become crucial measures for local governments to realize pollution reduction targets through fiscal expenditure. (3) Heterogeneity analysis reveals that the positive significance of decentralization is most prominent in the eastern China, while local governments with fiscal autonomy in central region tend to transfer pollution to neighboring cities. (4) There is a threshold characteristic for fiscal decentralization to promote a reduction in urban pollution intensity, and its marginal effect becomes more significant accompanied by continuous introduction of sophisticated foreign direct investment. Finally, the paper summarizes the potential significance of fiscal decentralization among Chinese local governments against the background of "Chinese-style decentralization" and proposes corresponding policy recommendations.

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Previous research has yielded mixed conclusions regarding whether business environment (BE) optimization can enhance carbon emission efficiency (CEE). This study delves into the impact of the BE on CEE using panel data from 30 provinces in China, employing fixed effect, quantile, and mediated effect models. It innovates in three key areas: research perspective, mechanism of action, and heterogeneity analysis. The research found that the BE optimization enhances CEE. Meanwhile, the influence of the BE on CEE exhibits marginal decreasing characteristics. The mechanism analysis reveals that the BE enhances CEE through the industrial structure optimization effect and the progress of green technology, while it diminishes efficiency through the energy rebound effect. Heterogeneity analysis indicates that BE optimization has a stronger impact on improving CEE in provinces with robust government governance, younger governors, and highly educated officials. The policy implication suggests that local governments should continually optimize the BE, enhance government governance capacity, and prioritize the appointment of young and highly educated officials.

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The optimization and transition of the industrial structure help improve the quality of the economy, moving it toward low-carbon development. By using the Instrumental Variable Generalized Method of Moments model and a city-level dataset covering the period 2006-2019, this paper explores the carbon lock-in mitigation effects of industrial structure optimization and industrial structure transition, respectively. The heterogeneity and the synthetic industrial structure adjustment effect are detected, and the potential impact mechanism is also explored. Some findings have been generated. (1) Both industrial structure optimization and industrial structure transition realize carbon lock-in eradication. (2) Industrial structure optimization and transition are more effective in inhibiting carbon lock-in in cities with a higher level of economic background. (3) By generating an interaction term of industrial structure optimization and transition and examining the impact of the interaction term on carbon lock-in, this paper detects that industrial structure optimization and transition have a synthetic impact on carbon lock-in, showing a "1 + 1 > 2" effect. (4) Industrial structure optimization and transition both affect carbon lock-in by improving technological innovation level, which is the impact mechanism. Some policy implications, such as sectoral diversification and promoting research and development, are put forward for better industrial structure development and carbon unlocking.

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Since the 1994 China's tax-sharing reform, land finance has far-reaching influence on fiscal system and has critical implications for green development and industrial structure optimization. Previous studies have largely focused on the environmental aspect of land finance by considering carbon emissions as environmental indicator only and ignored social and economic aspects. To fill the research gaps, this study analyzed spatial and moderating effects of land finance on green development through rationalized industrial structure, quantity of advanced industrial structure, and quality of advanced industrial structure in 246 Chinese cities from 2006 to 2019. The results document that local and surrounding land finance have negative effect on China's green development. Local land finance inhibits green development in western and large cities, while surrounding land finance deteriorates in eastern and large cities. Through mediation of industrial structure optimization, land finance accelerates green development in national, eastern, and large cities. In western cities, land finance promotes green development through quantity and quality of industrial structure upgrading. In small- and medium-sized cities, quantity of industrial structure upgrading plays a positive role.

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Promoting industrial structure optimization and improvement on the basis of environment regulation based on "China's carbon peak and carbon neutralization" turns out to be a requirement that cannot be avoided for achieving China's economic growth with high quality. In this study, a dynamic game model for enterprises and governments in local areas with two phases, covering a polluting production sector and a clean production sector, is built for analyzing the influence mechanism of environment regulation of local governments on industrial structure optimization. Panel data of 286 cities on the prefecture level and above from 2003 to 2018 served as samples. The direct and dynamic impacts of environment regulation on industrial structure optimization are empirically tested, and the threshold model is adopted to test whether industrial structures and resource endowment will affect the effect of environment regulation on industrial structure optimization. Lastly, the effect exerted by environment regulation on industrial structure optimization is tested by region. The empirical results show that there is a nonlinear correlation of environment regulation and industrial structure optimization. Once the environment regulation intensity reaches a certain inflection point, it will hinder industrial structure optimization. When regional resource endowment and the secondary industry's ratio are used as threshold variables, environment regulation has a threshold effect on industrial structure optimization. The effect exerted by environment regulation on industrial structure optimization has regional heterogeneity.

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This paper takes the panel data of 283 prefecture-level cities in China from 2011 to 2020 as the research sample, measures the comprehensive index of industrial structure optimization and air quality by using GRA-TOPSIS comprehensive evaluation method, explores the spatial and temporal divergence characteristics of industrial structure optimization and air quality and the spatial and temporal evolution pattern of coupled and coordinated development by using ArcGIS spatial analysis and coupled coordination degree model, and analyzes the driving factors of coupled coordination degree of industrial structure optimization and air quality by combining multi-scale geographically weighted regression model. The study found the following: (1) The overall level of China's urban industrial structure is low, and shows an obvious eastern > central > western decreasing trend; urban air quality has a strong spatial clustering and spatial locking effect. (2) During the study period, the coupling coordination degree of industrial structure optimization and air quality showed an inverted "W" shape fluctuation from 2011 to 2020. The coupling degree and coupling coordination degree in 2020 were both higher than that in 2011, and most cities were in the run-in stage and moderate coordination stage. (3) There is a consistency in the temporal evolution trend and spatial evolution pattern of industrial structure optimization and air quality coupling degree and coupling coordination degree. (4) The driving factors are ranked according to the scale of action: public transportation intensity > population density > government intervention > GDP per capita > industrialization level. At present, China is in a critical period of promoting high-quality development by ecological civilization, and it is recommended to optimize regional industrial structure, improve urban air quality, and promote coordinated urban development.

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The establishment of specific targets for the global carbon peaking and neutrality raises urgent requirements for prediction of CO2 emission performance indexes (CEPIs) and industrial structure optimization. However, accurate multi-objective prediction of CEPIs is still a knotty problem. In the present study, multihead attention-based convolutional neural network (MHA-CNN) model was proposed for accurate prediction of 4 CEPIs and further provided the rational suggestions for further industrial structure optimization. The proposed MHA-CNN model introduces deep learning mechanism with efficient resolution strategies for training model overfitting, feature extraction, and self-supervised learning to acquire the adaptability for CEPIs. Multihead attention (MHA) mechanism plays important roles in influence weight interpretation of variables to facilitate the prediction performance of CNN on CEPIs. The MHA-CNN model presented its overwhelmingly superior performance to CNN model and long short-term memory (LSTM) model, two frequently-used models, in multi-objective prediction of CEPIs using 8 influence variables, which highlighted advantages of MHA module in multi-dimensional feature extraction. Additionally, contributions of influence variables to CEPIs based on MHA analyses presented relatively high consistency with the geographical distribution analyses, indicating the excellent capacity of the MHA module in variable weights identification and contribution dissection. Based on the more accurate prediction results by MHA-CNN than those by CNN and LSTM model, the increase in the tertiary industry and the decreases in the first and secondary industries are conducive to improvement of total-factor carbon emission efficiency and further enhancement of effective energy utilization in regions with inefficient carbon emissions. This study provides insights towards the critical roles of the proposed MHA-CNN model in accurate multi-objective prediction of CEPIs and further industrial structure optimization for improvement of total-factor carbon emission efficiency.

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Under the "Digital China" strategy and "Carbon Peaking and Carbon Neutrality" goal, it is significant to explore the carbon reduction effect from the digital economy development in a multi-dimensional way. Based on the panel data of 13 cities in the Beijing-Tianjin-Hebei (BTH) region from 2011 to 2019, this study uses mechanism test model, threshold effect model, and spatial Durbin model which empirically test the influence mechanism and spatial spillover effect of digital economy development on regional CEI. The research found that (1) the digital economy development in the BTH region can reduce regional CEI, and it passes the endogenous test; (2) the digital economy indexes of 13 cities in the BTH region have significantly increased with time evolution, but there is obvious spatial unevenness; the CEI of each city except Tianjin decreases significantly with time evolution, and Tianjin shows a trend of decreasing and then increasing; (3) digital economy has a positive spatial correlation, showing the characteristics of "H-H" and "L-L" clustering. Furthermore, the digital economy has a spatial spillover effect on the CEI of neighboring cities; (4) the digital economy development can promote the industrial structure rationalization and upgrade, improves the urban green innovation quantity and quality, then reduces the regional CEI through them; and (5) the impact strength of digital economy on CEI varies at different threshold intervals of the mechanism variable.

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Along with the steady worsening of environmental contamination, improving environmental governance performance (EGP) is an urgent issue that needs to be addressed. This study adopts panel data from 275 Chinese cities to examine the holistic relationship between the digital economy (DE) and urban EGP in multiple dimensions. The relationship is tested using fixed-effects models, mediated effects models, panel threshold models, and the spatial Durbin models. Results indicated that (1) the DE can effectively improve urban EGP. The findings still hold after robustness tests using instrumental variable estimation and the introduction of exogenous policy shocks. (2) The analysis of mediating effects shows that green technology innovation and industrial structure optimization are critical transmissions. (3) The panel threshold regression results indicate a nonlinear incremental phenomenon of the significant impact of the DE on urban EGP when nighttime lighting data is used as the threshold. (4) Spatial spillover analysis shows a positive spatial spillover effect of DE development on EGP in the surrounding cities. (5) Spatial heterogeneity analysis states that the enhancement effect of DE development on EGP is more significant in eastern China. Furthermore, areas located within urban agglomerations are more influenced by the DE. These findings provide a policy reference for fully exploiting the environmental enhancement effects of the DE.

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The digital economy has demonstrated strong resilience and great potential, under the interwoven influence of the global pandemic and severe environmental concerns across the world. Therefore, there is a need to focus on the value of green economic growth in the digital economy. This paper constructs an evaluation index system and adopts the SEEA (System of Environmental and Economic Accounting) method to measure the digitalization level (Digi) and green economy growth level (GEG) of China. The internal mechanism and linear relationship between digitalization and green economy growth are examined based on the panel data from 2013 to 2019. Moreover, this study explores the spatial spillover effect. The major study findings are as follows: (1) Digitalization and green economy growth represent a steady growth trend, and the former as a whole significantly promotes the latter, with a marginal effect of 1.648. (2) The mechanism analysis indicates the intermediary effects' size of three crucial intermediaries: green technology innovation > advanced industrial structure > the rationalization of industrial structure. (3) Both the "local effect" (0.556; 0.574) and "neighboring effect" (1.382; 1.415) of digitalization on green economy growth are positive under the two weight matrices and display "simultaneous resonance" characteristics based on the spatial perspective. (4) There exists obvious regional spatial heterogeneity and resource endowment heterogeneity. Finally, this study put forward corresponding policy implications, such as construction of new digital infrastructures and guiding green-energy consumption.

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Effectively identifying the role and mechanism of green finance in environmental governance provides an important guarantee that green finance serves the ecological environment. Based on the panel data of 30 provinces in China from 2001 to 2015, this paper explores the impact of green finance on cleaner industrial production and end-of-pipe treatment and further reveals the mediating effect of industrial structure optimization and the moderating effect of environmental regulation. The results show that (1) China's cleaner industrial production performance, end-of-pipe treatment performance and systematic governance performance show a clear upwards trend, distributed in stages in the eastern, central and western regions from high to low. (2) At the national level, green finance promotes cleaner production performance but inhibits end-of-pipe treatment performance. However, it can be seen from the results of the sub-sample, the eastern and central regions are consistent with the overall effect, with heterogeneous effects in the western region. (3) The optimization of industrial structure plays a partial intermediary role in the impact of green finance on cleaner production and end-of-pipe treatment. (4) Both "market-incentive" and "command-and-control" environmental regulations weaken the positive impact of green finance on cleaner production; "market-incentive" environmental regulation alleviates the negative impact of green finance on end-of-pipe treatment, while the moderating effect of "command-and-control" environmental regulation on end-of-pipe treatment is not significant.

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Local government intervention in land resource allocation can lead to the misallocation of land resources and serious pollutant emissions. As an important market-oriented economic reform in China, the marketization of urban land transfer (MULT) might have the potential to contribute to improving resource allocation efficiency by curbing local government intervention. Therefore, this study aims to provide empirical evidence on the impact of MULT on energy efficiency. We improve the MULT evaluation method to test the mechanism through which MULT affects energy efficiency. The results show that, first, the proportion of land sold by allocation and listing methods, which is characterized by a low degree of marketization, has rapidly increased in recent years, lowering the overall level of MULT. Second, MULT has a direct and significant positive impact on improving energy efficiency. Third, the mechanism analysis indicates that MULT helps enhance energy efficiency by advancing industrial structure optimization and technological progress. Moreover, the heterogeneity analysis demonstrates that the impact of MULT on improving energy efficiency differs significantly in different reform stages and between central and peripheral cities. This study sheds light on the importance of land resource allocation in improving energy efficiency and thus has practical policy implications for promoting low-carbon energy transition in emerging countries.

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Based on the panel data of 278 prefecture-level cities in China from 2005 to 2017, this study takes the environmental information disclosure policy implemented in 2008 as a quasi-natural experiment and uses the difference-in-difference method to examine the impact of public environmental supervision on industrial structure upgrading. This study found that public environmental supervision has indeed significantly promoted the industrial structure optimization. It is noteworthy that, the impact of public environmental supervision on industrial structure optimization is heterogeneous across regions and corresponding to the intensity of environmental regulations. Public environmental supervision in the eastern region has significantly promoted the optimization of the industrial structure, while the promotion effect in the central and western regions is not obvious. Besides, the effect of public environmental supervision is more significant in the cities with relatively tight traditional environmental regulations. The mechanism analysis shows that public environmental supervision promotes industrial structure upgrading mainly by influencing the level of urban technological innovation, and the level of technological innovation plays an intermediary role between public environmental supervision and industrial structure optimization. This study has important reference significance for further improving the environmental information disclosure system and constructing an environmental governance system with the government as the leading body, enterprises as the main body, and the public as the common participation.

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Global warming caused by greenhouse gas emissions seriously threatens a region's sustainable environmental and socioeconomic development. Promoting industrial restructuring and strengthening technological innovation have become an important path to achieving pollution and carbon reduction as well as the green transformation of economic structure. This paper explored the mechanism of the mediating effect of technological innovation on industrial restructuring and carbon reduction while accounting for the direct effect of industrial restructuring on carbon emissions. Then, based on China's provincial panel data from 2001 to 2019, we estimated the carbon emission intensity using the Intergovernmental Panel on Climate Change (IPCC)'s methods and analyzed its spatiotemporal evolution characteristics. Finally, we constructed a fixed-effect model and a mediating effect model to empirically analyze how industrial restructuring and technological innovation affect carbon emission intensity. The results are as follows: (1) From 2001 to 2019, China's carbon emission intensity showed a continuous downward trend, with a pronounced convergence trend; there were obvious differences in carbon emission intensity between eastern, central, and western regions (western region > central region > eastern region) due to the unbalanced industrial structure. (2) In terms of direct effects, industrial restructuring can significantly reduce carbon emission intensity. The intensity of the effect is inversely proportional to the level of industrial restructuring, and the results of sub-regional tests are similar. Nevertheless, there is an obvious regional difference in the size of the carbon emission reduction effect of industrial restructuring in the east, central, and western regions. (3) In terms of indirect effects, industrial restructuring can reduce carbon emission intensity by enhancing technological innovation, and it acts as a mediating variable in the process of industrial restructuring to reduce carbon emission. Finally, we put forward recommendations for promoting industrial restructuring, strengthening green technological innovation, and properly formulating carbon reduction measures to provide a reference for countries and regions to achieve the goals of carbon neutrality, carbon peaking, and high-quality economic development.

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In the post-COVID-19 period, the regional economic development gap in Guangdong has expanded and the risk has increased. This is not conducive to building a well-off society in Guangdong in an all-round way. Optimizing the industrial structure, changing the mode of economic growth, preventing regional economic risks, and narrowing the regional economic development gap are the focus of Guangdong governments at all levels. Based on a two-sector economic growth model, the paper uses descriptive statistics and Deng's grey correlation method to analyze the economic development panel data of 21 prefecture-level cities of Guangdong Province from 2011 to 2018, analyzes the characteristics of regional economic development in Guangdong, and makes experimental research of the effect of industrial structure optimization and economic development factors on economic growth in Guangzhou, Zhongshan, Shantou, Maoming and Meizhou at different stages of development. The results show that: there are differences in the industrial structure and the level of economic development in different regions; the rationalization of industrial structure and upgrading of the industrial structure has an impact on economic growth and show different features during different stages; in different stages of economic development, foreign trade, scientific and technological innovation are the main factors affecting regional economic growth. This paper holds that the northwest of Guangdong Province and the Pearl River Delta should formulate industrial structure adjustment policies to promote the rationalization of regional industrial structure, expand foreign trade channels and innovate financing mechanisms, and restrain regional economic risks in combination with regional characteristics. The new development pattern of domestic and international dual circulation and mutual promotion, further expands the level of opening up, effectively playing the role of free trade pilot areas, free trade ports, special economic zones, development zones, bonded areas, and other frontier highlands, improve the level and capacity of foreign trade.

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Although the literature has studied the direction and extent of the effect of green finance on industrial-structure optimization, quantitative analysis of the coupling coordination and spatial-temporal differences between green finance and industrial structure is relatively scarce. Therefore, in this paper, we built the theoretical framework of the coupling coordination relationship between green finance and industrial-structure optimization, and then we used the coupling coordination degree and geographic detector model to investigate the spatial-temporal evolution characteristics and influencing factors of the coupling coordination between the two based on the panel data of 31 provinces from 2012 to 2019. The results show that China's green finance and industrial-structure optimization have basically reached the primary coupling, and the coupling coordination degree is from 0.40 to 0.43, which shows a "W"-type fluctuation trend of recovery. The regional gap of the coupling coordination degree firstly decreased and then increased, showing a change law of "agglomeration, equilibrium and agglomeration". In the spatial dimension, the high-level coordination region showed an increasing trend of "fragmentation" fluctuation, while the low-level coordination region concentrated in the central and western regions with a tendency of "low value locking". The trend surface showed a spatial characteristic of "high in the north and low in the south-high in the east and west and low in the middle". We also found that the dependence of foreign trade and technological innovation are the main factors affecting the coupling coordination degree, and the interaction between government support and human capital synergistic is the crucial channel for the coevolution of green finance and industrial structure to promote green and low-carbon development.

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In this study, the central environmental protection inspection (CEPI) policy is considered a quasi-natural experiment. Based on the data of 216 Chinese cities from 2008 to 2018, the influence of CEPI on green technology innovation (GTI) is empirically examined mainly using difference-in-differences (DIDs), propensity score matching DID and spatial DID methods. The results indicate that CEPI can effectively promote GTI. Regarding different types of GTI, CEPI mainly promoted utilitarian GTI. Regarding the mechanism, CEPI significantly promotes local GTI mainly through the increase of environmental protection expenditure and research and development investment. Considering the dynamic marginal effect, CEPI starts to significantly promote GTI in the second year after the policy implementation but exhibited no effects in the third year. The extended study shows that GTI effect of CEPI only occurs in small-medium cities and big cities. Furthermore, there is a certain beggar-thy-neighbor effect between inspected and uninspected cities. Finally, the spatial decomposition of CEPI effects shows that the inhibitory effect of CEPI on GTI mainly occurs in the neighboring uninspected cities, while CEPI has no evident inhibition on GTI in neighboring inspected cities.

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Although many scholars have focused on industrial structure adjustment to find the optimal balance between carbon emission reduction and economic growth, few studies have considered the comprehensive influence of the supply chain structure on carbon emissions. Based previous studies, we proposed a novel network-based optimization model. The results showed that carbon emissions would decrease by 4.31%, 6.26 and 8.07% with GDP increasing by 5.53%. 4.45% and 2.50% in different scenarios in the proposed network-based model, which performed better than the previous non-network-based model. There were some principal sectors which played special roles in the optimization of global industrial structure. To achieve the goal of global carbon emission reduction, some sectors should significantly reduce their total outputs such as materials and energy in China, while other sectors could continue to increase their total outputs such as machinery and services in China and South Korea. The results also showed that the change rate of carbon emissions was related with the costs of carbon emissions for the GDP growth. Countries with higher costs of carbon emissions, such as China, India and Russia, would burden more responsibilities. Furthermore, we found that the changes of the industrial structures of countries (regions) were different under global carbon emission reduction. Due to the current technology limitation, the production activities of energy and material industries in developing countries, such as China and India, should be reduced. Technology exportation of developed countries in such industries would be beneficial for the global carbon reduction.

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Industry structure adjustment is an important way to solve environmental problems. The adjustment of the industrial structure across regions not only needs to meet the goals of each region, but also involves the industrial transfer between regions. The same industry in each region has differences in economic development, energy consumption and carbon emissions. So these regions can reasonably distribute industries in various regions through the industrial transfer to meet their own requirements. A cross-regional multi-objective planning model combined the data envelopment analysis method is put forward to solve the problem of the reasonable industries distribution. The representative result which is selected from the set of different preference solutions reflects the coordination of economic development and environment. In order to distinguish the effects, the results of cross-region and single-region industry structure optimization models are compared. The Yangtze River Delta as a case study is analyzed. The results show that each province can realize the reasonable distribution of industries through the industrial transfer, and complete the phased goals in the 13th Five-Year Plan. Meanwhile, the eco-efficiency of each province is improved and the difference in economy has narrowed. The case provides a basis for other regions to balance development of economy and environmental protection through regional cooperation and division of labor.