Spatial-temporal heterogeneity of the coupling between digital economy and ecological efficiency and its influencing factors in China

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It is the catalyst for sustainable economic growth and high-quality development in China. Specifically, the study applies modified E-G index, super-efficiency slacks-based measure (SBM) with Malmquist-Luenberger (ML) index, entropy weight Topsis, coupling coordination degree and other models to explore the spatial-temporal heterogeneity of the coupling between digital economy and ecological efficiency. In addition, the internal mechanism of coupling is analyzed from the dimensions of industrial collaboration, technological innovation, environmental regulation, and other aspects. The results show that the coupling between digital economy and ecological efficiency is an upward trend from imbalance to synergy in China on the whole. The distribution of the coupling at the synergistic level expanded from point-like to band-like, and the pattern of spreading from east to the center and west was significant. The number of cities in the transitional level decreased significantly. It can be seen that the jump phenomenon and linkage effect of coupling in space and time are significant. Additionally, the absolute difference among cities has expanded. Although the coupling in the west has the fastest growth rate, the coupling of the east and resource-based cities still has obvious advantages. Therefore, the interaction of systems has not reached the ideal coordinated state, and a benign interaction pattern has yet to be formed. Industrial synergy, industrial upgrading, government support, economic foundation, and spatial quality all show positive effect on promoting the coupling of digital economy and ecological efficiency; technological innovation reflects a certain lag; environmental regulation that has not been fully exerted needs to be used scientifically and accurately. Among them, the positive effects of government support and spatial quality performed better in the east and non-resource-based cities. Because of the continuous optimization of the industrial level, the coupling between the west and resource-based cities has achieved better dividends, but the spatial quality needs to be further improved. Therefore, the efficient coordination of China's digital economy and ecological efficiency urgently needs scientific, reasonable, localized, and distinctive manner. Digital economy Ecological efficiency Coupling Influencing factors China Figures Figure 1 Figure 2 Figure 3 Introduction In the context of dual carbon, in order to achieve high-quality development, China urgently needs to pay attention to the contradictions of "high input, high consumption, high pollution, low quality, low benefit, and low output." Implementing the concept of ecological efficiency and pursuing a green economy has become the fundamental strategy to coordinate the conflict between environmental pollution and economic growth (Zhang et al., 2022; Ahmad and Wu, 2022). Simultaneously, the digital economy such as 5G, cloud computing and artificial intelligence, as a manifestation of the quality of economic development, has not only become a new driving force, but also a key support for improving ecological efficiency. Therefore, developing the digital economy and improving ecological efficiency has become a common choice for many countries to recover and develop after the epidemic. Then, exploring the heterogeneous law and influence mechanism of the interaction between digital economy and ecological efficiency is of great significance for giving play to the green value of the digital economy, and promoting the high-quality development of the economy. Therefore, the more pressing question is, how is the interaction between China's digital economy and ecological efficiency? Are there heterogeneity features? In addition, what is the interaction mechanism affecting the two systems? This study addresses the above questions. The contributions of this research are listed below. At the theoretical level, the existing research on ecological efficiency is mostly based on the analysis of the environment, industry, technology and other dimensions, while there are few researches on the prefecture-level city scale combined with the background of the digital economy. The existing literature also does not pay attention to the interaction between the two. This study introduces an interactive framework from the perspective of digital background on the issue of ecological efficiency, which shows a new research perspective for the synergy between digital economy and ecological efficiency. At the same time, it enriches and expands the theoretical content of the synergistic interaction of digital economy and ecological efficiency. At the practical level, this study more comprehensively analyzes the internal mechanism of the coupling, which is from the aspects of industrial collaboration, technological innovation, environmental regulation, industrial upgrading, spatial quality, economic foundation, and government support. Heterogeneity analysis was carried out from the perspective of geographical location and resource type. This will help clarify the boundary conditions of the digital economy and ecological synergy, and provide a useful reference for the government to take targeted measures. Therefore, this study attempts to explore the heterogeneity of the coupling between digital economy and ecological efficiency from 2011 to 2020 in China, and to analyze the internal mechanism that affects their synergy in multiple aspects. This provides useful experience support and decision-making reference for unleashing the potential of the digital economy, improving ecological efficiency, and achieving dual-carbon goals and high economic quality. For the rest of this manuscript, the second part “Literature review” summarizes the relevant literature; the third part “Methodology” specifically elaborates the mechanism of the coupling between digital economy and ecological efficiency, expounds specific models, influencing factors, and the data source; the fourth part “Results and discussion” reveals the spatial-temporal heterogeneity of the coupling, probes into the internal mechanism that affects the synergy of the systems, and then makes discussions; the fifth part “Conclusions and suggestions” concludes the research and puts forward corresponding suggestions. Literature Review Regarding the research on ecological efficiency, the Organization for Economic Cooperation and Development (OECD) believes that ecological efficiency refers to the efficiency with which ecological resources meet human needs. Its core is to achieve economic growth based on resource conservation and environmental improvement, and it has become an important engine for the country's leading development (Borel-Saladin and Turok, 2013; Mustafa et al., 2021). Simultaneously, it is an effective indicator that can measure the coordination interaction of economy and environment systems from the perspective of energy conservation and emission reduction (Nkengfack and Fotio, 2019; He et al., 2022). It not only provides an intuitive effect for the new economic development model, but also provides a basis and guidance for policy adjustment. In terms of research methods, it mainly includes the implementation of green national economic accounting, the construction of a comprehensive evaluation index system, and the calculation of green total factor production efficiency, etc., to comprehensively evaluate the ecological efficiency (Abdallah et al., 2015; Zhang et al., 2020; Liu et al., 2022). As far as research hotspots are concerned, in addition to exploring the laws of spatial-temporal heterogeneity and agglomeration of regional ecological efficiency, existing research mostly focuses on the impact of digital technology, environmental policies, and ecosystem services on ecological efficiency (Zhang et al., 2020; Han and Chen, 2022). For China, it emphasizes its basic concepts and principles, focusing on the discussion of the green economy brought about by efficiency improvements (Wei et al., 2020). With regard to the digital economy, scholars emphasize that the digital economy is a series of economic activities driven by digital technology to optimize economic structure and improve efficiency. The most important feature of the digital economy is that it can break the spatial- temporal boundaries of factor flows and enhance the breadth and depth of inter-regional economic activities. At present, the research is biased, and most of them focus on the conceptual and theoretical level. The definition, development trend and promotion policies of digital economy are discussed. In addition, there are also studies exploring the laws of economic development in the context of the digital economy (Watanabe et al., 2018; Grybauskas et al., 2022). Overall, the literature on quantitative research is relatively weak and mostly at the national and provincial level. As attention to the digital economy continues to heat up, the digital economy is developing rapidly. The economic effects and high penetration of the digital economy, as well as the obvious spatiotemporal heterogeneity, are gradually being explored (Zhou and He, 2020). In addition, some scholars have conducted in-depth discussions on the positive effects of the digital economy, such as industrial optimization (Liang et al., 2020), technological upgrading (Han et al., 2019), production efficiency (Watanabe et al., 2018), pattern optimization (An and Yang, 2020), high-quality economic development (Zhao et al., 2020; Akbari and Hopkins, 2022), and other aspects. Given that the digital economy itself is an environment-friendly industry, it can effectively reduce resource and environmental input by improving production efficiency, promoting collaboration efficiency, and stimulating innovation efficiency. Ultimately, the ecological efficiency of the city will be improved (Jiang, 2021; liu et al., 2022). It is an inevitable choice for the economy to seek opportunities and motivation from ecological efficiency to achieve high-quality transformation and sustainable development (Rickard et al., 2017; Xu et al., 2022). There are many studies on the relationship between economy and ecology, the earlier ones are theoretical studies, such as circular economy theory (Beckerman, 1992), environmental Kuznets curve (KFC) theory (Grossman and Krueger, 1995). However, there are few studies on the relationship between digital economy and ecological efficiency, and most of them focus on the theoretical level (Fahmi and Sari, 2020). In addition, current related research mostly emphasizes the positive effect of the digital economy in improving ecological quality, and the digital economy has gradually become a key driving force for improving ecological efficiency (Yu and Zhu, 2022; Han and Chen, 2022; Dunlap and Laratte, 2022). Specifically, the studies involved can be grouped into four categories. First, by analyzing the technological enabling means of the digital economy to promote industrial transformation, improve the efficiency and quality of the supply system, and then improve ecological efficiency (Belitski et al., 2021). Second, explore the positive effect of the digital economy on saving resources and protecting the environment from the perspective of resource utilization efficiency (Barykin et al., 2020; Ma et al., 2022; Wang et al., 2022). Third, analyze the digital economy from the perspective of technological innovation efficiency and production model improvement to change the extensive growth mode and achieve ecological quality improvement (Han et al., 2019). Fourth, discuss the positive effects of digital economy development on leading the formation of low-carbon life and consumption patterns based on a green lifestyle (Zhou and He, 2020; Kovacikova et al., 2022). With the continuous progress and application of digital technology, empirical research on the positive effects of digital economy on the spatial spillover of ecological improvement has increased (Yuan et al., 2021; Zhao et al., 2022; Guo et al., 2022). At the same time, the mechanism of the digital economy on ecological efficiency is gradually being revealed (Jensen et al., 2021; Gai et al., 2022). It can be seen that the arrival of the digital economy era is not only the improvement of ecological efficiency led by technological innovation, but also the green development caused by power conversion, structural upgrade and efficiency change (Rysina, 2021; Liu et al., 2022). To sum up, in the context of China's high-quality development, the pursuit of synergy between the digital economy and ecological efficiency can inject strong momentum into high-quality development (Cui et al., 2020). Scholars have conducted sufficient research on ecological efficiency and digital economy, and many scholars have explored the impact of digital economy development on ecosystems at the provincial level. However, few scholars study the cooperative interaction law of the two systems. Most importantly, existing research has not discussed in depth the ecological value permeating the digital economy at the prefecture-level city scale. That is, the mechanism that affects the coordinated development of the digital economy and ecological efficiency about cities has not thoroughly been analyzed. Therefore, this paper explores these issues in more depth. Methodology Mechanism analysis Based on the network effect theory, innovation theory, endogenous growth theory, green growth and other theoretical foundations, the law of coupling between digital economy and ecological efficiency system is analyzed. In addition, fully excavating the influencing factors of coupling and effectively promoting a virtuous cycle of the system is a catalyst and an important characterization for realizing regional high-quality coordinated development (Pouri and Hilty, 2021; Dunlap and Laratte, 2022; Yu and Zhu, 2022). The specific mechanism is shown in Figure 1. The digital economy works from both the supply side and the demand side to effectively maintain ecological functions and provide new opportunities for improving ecological efficiency (Wang et al., 2020; Cai and Wang, 2022; Petrov, 2019). On the one hand, the digital economy has created space and new channels for the free flow of factors. By reducing search, transaction and matching costs, etc., the distortion of factor allocation is optimized, thereby improving ecological efficiency. On the other hand, big data platforms can monitor production in real time and use aggregated data to accurately predict input and output. The government or enterprises can form a scientific monitoring system, realize intelligent upgrading, and promote the coordinated development and virtuous circle of digital economy and ecological environment (Brown & Mcgranahan, 2016). In addition, the ecological environment provides a material basis for the sustainable development of the digital economy (Wang et al., 2017). When the ecological environment is affected by economic activities, it will also have a feedback effect on economic development. Especially when the ecological damage is more serious, problems such as energy constraints and resource shortages will restrict economic development. Only a good ecological environment can provide a sustainable material source for economic activities (Vaz et al., 2017). Therefore, in order to achieve sustainable economic development and improvement of ecological efficiency, it is urgent to promote the coordinated development of the digital economy and ecological efficiency, and realize the dialectical unity of the two. In the process, the coupling of digital economy and ecological efficiency is affected by multiple levels. To promote the synergy between the two, the following factors should be paid special attention. Industrial synergy strengthens the cleaning function of the producer service industry for the manufacturing industry, which is conducive to the formation of a green and resilient industrial chain. While stimulating the growth of the digital economy, this factor strengthens the input level and output capacity, and promotes the economy to move towards a low-pollution, low-energy-consuming growth model (Cai and Wang, 2022; Li et al., 2021). Technological innovation continues to inject innovative impetus into the synergy of digital economy, which can penetrate into labor, capital, etc. through the continuous "multiplier" effect, ensuring efficient output and ecological quality. Environmental regulation will have a forcing effect and can become an accelerator for the digital economy and green transformation. Strengthening control in the process of input and output can effectively improve ecological efficiency to a certain extent (Petrov, 2019; Yu and Liu, 2021). Industrial upgrading is manifested as the birth of new industries and new formats. The "structural dividend" brought about by the upgrading of industrial results improves ecological efficiency while benefiting economic growth (Shen and Huang, 2020). As the key regulator of the digital economy and ecological efficiency, the government plans the economic direction and manages the ecological environment. Its macro-control, especially financial resources, has a strong impact on the interaction between the digital economy and ecological efficiency (Tang et al.,2022; Spence, 2021; Shahbaz et al., 2022). Therefore, the synergy of green economy and ecological efficiency is beneficial to the realization of green, low-carbon and sustainable growth of the digital economy. Thus, in China, the research on the heterogeneity of the coupling between digital economy and ecological efficiency and its internal mechanism will help to promote the harmonious coexistence of the economy and the ecosystem. Specific model Digital economy Currently, there is no unified standard to comprehensively measure the level of the digital economy. Drawing on the research of Zhao et al. (2020), this study evaluates the development of the digital economy, mainly from the two aspects of digital finance and Internet development. Specifically, the number of Internet users, the proportion of computer service and software practitioners, the total telecommunications business per capita, the per capita postal service, the number of mobile phone users, and the digital financial inclusion index are all selected as the indicator system for measuring the digital economy. After comparing with the principal component analysis method, the comprehensive evaluation index of digital economy is finally calculated by using the entropy method. Ecological efficiency The core of ecological efficiency improvement is to achieve the greatest possible economic growth at the least resource and environmental cost, that is, the improvement of green growth efficiency. Green total factor productivity is a comprehensive economic efficiency that considers the cost of resources and the environment, an important indicator to measure the quality of economic growth, and the key to achieving a win-win situation for regional ecology and economy. Therefore, this study uses this indicator as a proxy variable of ecological efficiency. Specifically, based on the SBM model, the Malmquist-Luenberger (ML) index model for constructing undesired outputs is used to measure green total factor productivity (Zhang et al., 2020; Shah et al., 2022), and Max DEA 8 software is used to measure this index. The specific input and output indicators are selected as shown in Table 1. Table 1 The index system of ecological efficiency Indicator type Indicator selection Indicator description Input Labor Number of employees in the unit/person Capital Capital stock/yuan (estimated by perpetual inventory method, depreciation rate is 9.6%) Energy Annual total electricity consumption/ kWh Output Expected output GDP/yuan Expected output Industrial wastewater discharge / ton Industrial sulfur dioxide emissions/ton Industrial solid waste discharge / ton Coupling coordination degree The concept of coupling can reflect the dynamic relationship among systems. Drawing on related research, this study uses coupling coordination degree model to measure the coupling level (Zheng et al., 2022; Ma et al., 2012). The coupling degree is C, and the larger C is, the higher the coupling. The specific formula is as follows. Among them, U 1 and U 2 represent the digital economy and ecological efficiency evaluation index respectively. The coordination degree model is constructed on the basis of the coupling model to reflect the coordination degree of the two systems. The formula is as follows: Influencing factors selection By referring to relevant literature, the following influencing factors were finally selected. Industry collaboration ( CXT ). Industrial synergy reflects the degree of integration of the manufacturing and service industries, permeates the power of the digital economy, and promotes the improvement of ecological environment functions. This study uses the aggregated data of the mining and manufacturing industries, as well as the data of the employees of the producer service industry, and draws on the revised E-G index method to measure the level of industrial collaboration through the industrial synergy agglomeration index. For specific industries, please refer to the literature (Chen et al., 2016). Technology Innovation ( JSC ). It is a key manifestation of the digital economy and an important driving force for ecological improvement. Patents are the embodiment of science and technology, as well as the characterization of innovation. Therefore, this study uses the sum of the three types of patent grants (invention, utility model, and design) to measure the technological innovation of a city, reflecting whether technological development activities are active. Environmental Regulation ( HJG ). Appropriate intensity of environmental regulation stimulates digital innovation, drives industrial upgrading, and promotes high-quality ecological development. Considering the problem of data acquisition of prefecture-level cities, this study finally selected the indicators of sewage treatment rate, green space rate and domestic waste treatment rate in built-up areas, and calculated the intensity of environmental regulation based on the entropy weight TOPSIS (Zhu, 2020). Industrial upgrading ( GJH ). Industrial optimization and upgrading are the guarantee for the transformation of the digital economy to green development, and is the foundation for the improvement of ecological quality (Ren and Du, 2021). Referring to the method of Fu (2010), this study uses the industrial advanced index to measure industrial upgrading, which reflects the in-depth development level from low-level to high-level industries. Government support ( ZFL ). The government plays a macro-control role in both the digital economy and ecological efficiency, of which the most important is fiscal support. This can effectively make up for the shortcomings of the market, promote the efficient allocation of resources, and improve ecological efficiency. Therefore, this study uses the proportion of the government's public fiscal expenditure to GDP to measure the government's macro-control level. Economic foundation ( JFZ ). The economic foundation is the basic guarantee for the development of the digital economy and the basic condition for ecological improvement. Differences in economic foundations lead to differences in the development of the digital economy, which will also affect the improvement of ecological quality. This study selects the most typical indicator, that is, using GDP to measure the economic base. Spatial Quality ( KJP ). The quality of space affects the city's ability to attract investment and population, reflecting the basic platform support of space infrastructure for economic and ecological development. This study uses the number of employees in the transportation, warehousing and postal industries to measure spatial quality. Data sources In this study, the data of cities with serious missing data, such as Bijie and Tongren, were deleted. Considering that China's municipalities are provincial-level administrative units, they are significantly higher than ordinary prefecture-level cities in terms of politics, economy, and population. As a result, the development of the digital economy may be quite different from that of ordinary prefecture-level cities. In order to make the samples more comparable and the test conclusions more robust, prefecture-level administrative regions are uniformly selected as the research object. Therefore, the time range of this study is 2011-2020, and the final sample size is 2750, covering a total of 265 prefecture-level cities in China. The data mainly come from China City Statistical Yearbook, China Energy Statistical Yearbook, China Regional Economic Statistical Yearbook and statistical yearbooks of various cities. Among them, the data of general industrial solid waste and industrial sulfur dioxide are from the "Environmental Statistical Yearbook"; the industrial wastewater is from the provincial statistical yearbook; the carbon emission data is from the CEADs database; the number of patent authorizations is from the CNRDS database; the digital financial inclusion index is obtained from the Digital Finance Center of Peking University. In addition, some missing data in individual years were filled with the method of interpolation. Results And Discussion Temporal heterogeneity of the coupling From 2011 to 2020, the coupling coordination degree of digital economy and ecological efficiency showed some fluctuations, but it was almost a straight upward trend, and the overall situation was floating around 0.55 in China (Fig. 2). The interaction manifests itself as the evolution from a transitional state on the verge of dissonance to a coordinated development. And finally entered a collaborative state in 2019. In terms of cities, Guangzhou, Hangzhou, and Xiamen have the best coupling, with average values of 0.75, 0.74, and 0.73, respectively. Zhengzhou, Xiamen, Pingliang and other cities showed a faster growth rate, higher than 55%. This shows that under the Belt and Road strategy, international cooperation in digital technology has been carried out, the construction of digital infrastructure along the route has been accelerated, and cooperation in the field of digital interconnection has continued to deepen. As a result, the digital transformation of the industry has been promoted, and the digital economy and ecological efficiency of these cities have strengthened the penetration and interaction. During the study period, the number of cities in dysregulation decreased from 21 to 2; the number of cities in synergy increased from 1 to 156; the number of cities in the transition period decreased from 205 to 9, showing a significant decrease; the number of cities in the adaptation period increased from 48 to 108, and the synergy has emerged. As far as the difference among cities is concerned, the absolute difference shows a fluctuating upward trend, while the relative difference fluctuates steadily around 0.10. This reflects that the differences in the development of digital economy and ecological efficiency among cities continue to widen, resulting in significant temporal heterogeneity in the interaction between the two systems. This also shows that although the coupling between the digital economy and ecological efficiency of prefecture-level cities in China is increasing, there is still a lot of room for improvement. From the perspective of geographical location, the coupling of digital economy and ecological efficiency in eastern, central and western cities of China has shown an upward trend from 2010 to 2020. Both the absolute difference and the relative difference are relatively stable, floating around 0.04. Relatively speaking, the coupling in the east continues to occupy a significant advantage, with an average value of 0.57, and has entered the ranks of coordinated development after 2018. The cities in the central region increased from 0.45 in the early stage to 0.60 in the late stage. Although they gradually evolved from a transitional state to a synergistic edge, their growth rate was the lowest in the early stage, only 32.85%. The coupling fluctuations in the western region are the most frequent, but the late stage also shows coordinated development, and the growth rate in the late stage is the highest compared with the initial stage, reaching 37.05%. This shows that with the in-depth development of policies such as the rise of the central region, the development of the western region, and the Belt and Road Initiative, the development of the center and west has gradually been influenced and helped by the eastern region. The coupling of digital economy and ecological efficiency in the three major regions of China has risen relatively synchronously, but the stable and coordinated development of each region has not yet been achieved. In terms of resource types, during the study period, the coupling of digital economy and ecological efficiency in non-resource-based cities has an advantage, and it is always better than resource-based cities, with an average value of 0.56. Non-resource-based cities entered a state of coordinated development in 2018, while resource-based cities entered into synergy at the end. In addition, the difference between the two types of cities is relatively small and stable, with the absolute difference floating around 0.03. It can be seen that non-resource-based cities lack traditional energy and are more inclined to seek new driving forces for digital development. The rapid development of the digital economy and the improvement of ecological efficiency are more easily reflected, resulting in more active interaction between the two than resource-based cities. Spatial heterogeneity of the coupling From 2011 to 2020, the coupling of digital economy and ecological efficiency showed a clear spatial jump and linkage pattern. Fig. 3 shows that in the early stage, the coupling of most cities in China was dominated by transitional level, and the interaction between the two was on the verge of dysregulation. In detail, only Shenzhen was the first to step into the collaboration. Moreover, there are few cities where the coupling is in the adaptation period or barely coordinated, and the distribution is more fragmented. Cities at this coupling level are mostly on the east coast. Cities in the transition period are mostly distributed in clusters, and cities in dysregulation are mostly scattered in the central and western regions. The coupling in the east is obviously better than that in the center and the west. It can be seen that the spatial heterogeneity of coupling is obvious, and the development of the digital economy as a whole is not synchronized with the ecological efficiency. In the later stage, the coupling of digital economy and ecological efficiency in most cities has entered a coordinated level, and the number of cities at the coordinated level has increased significantly. In detail, cities in synergy are mostly distributed in bands, cities in adaptation are distributed in sheets, and cities in transition are distributed in points. Additionally, the diffusion path generally shows the characteristics of spreading from the developed areas in the east to the underdeveloped areas in the central and western regions, which reflects the driving effect of the east to a certain extent. Therefore, there is a certain spatial heterogeneity in the coupling between the digital economy and ecological efficiency during the study period. The significant coupling gap caused by the unequal development opportunities among regions still needs to be further filled, and the benign resonance pattern of the two systems needs to be further promoted. Influencing factors Model building Based on all-round selection of influencing factors, we verified the heterogeneity of the effects of these factors on coupling. Specifically, the equation between the coupling of digital economy and ecological efficiency and its influencing factors is expressed as follows: Table 2 Regression results of the factors influencing the coupling Geographical location Resource-based city or not Overall East Centre West Yes No CXT 0.0294*** 0.0342*** 0.0164* 0.0433*** 0.0241*** 0.0350*** (-6.2002) (4.2797) (2.2851) (4.3934) (3.3382) (5.4511) JSC 0.0001 -0.000262 0.000375 0.000249 0.000468 -0.0000783 (0.2437) (-0.3309) (0.5139) (0.2282) (0.6372) (-0.1204) HJG -0.0389*** -0.0652*** -0.0155 -0.00315 -0.0542*** -0.0174 (-4.2059) (-4.3473) (-1.0942) (-0.1525) (-3.8916) (-1.4016) GJH 0.0887*** 0.0932*** 0.112*** 0.0784*** 0.0988*** 0.0753*** (14.7801) (8.0245) (12.2764) (6.8871) (12.4524) (8.4153) ZFL 0.419*** 0.404*** 0.349*** 0.397*** 0.393*** 0.444*** (23.1616) (9.2101) (14.1768) (10.8072) (16.0560) (16.4902) JFZ 0.109*** 0.126*** 0.0757*** 0.139*** 0.0824*** 0.131*** (29.6606) (19.8140) (13.6906) (17.8252) (15.5143) (25.5706) KJP 0.0026** 0.0076*** 0.0012 0.0008 0.0016 0.0022* (3.1475) (3.5213) (1.1544) (0.5509) (0.4029) (2.4933) R-squared 0.6390 0.6478 0.6380 0.6903 0.6093 0.6674 Obs 2750 1090 1070 590 1110 1640 Note: “ *, **, *** ” indicate the statistical significance level of 10%, 5% and 1% respectively. Regression results Using the above model, the study explores the heterogeneous effects of factors on the coupling between digital economy and ecological efficiency in China. The specific analysis is as follows. Industrial collaboration . The overall regression coefficient of industrial collaboration on the coupling of digital economy and ecological efficiency is positive, and the P value is less than 0.01, which is significant at the 1% significance level. This shows that while the collaboration of producer services and manufacturing is enhanced with the help of the digital technology, it simultaneously promotes ecological efficiency. The synergy between industries permeates the interaction, which contributes to the synergy of the two systems. From the perspective of geographical location, although industrial synergistic agglomeration has an economic effect on the east, centre and west, it has the strongest positive effect on the west and the weakest in the centre. The reason is that the west is relatively backward, the dividends of the digital economy are relatively more significant, and the cleaning function of the producer service industry for the manufacturing industry has been significantly improved, resulting in a relatively prominent positive effect on coupling. While the central cities are actively accepting the transfer of traditional industries in the east, the role of industrial synergy has not been fully reflected. In terms of resource types, both resource-based cities and non-resource-based cities are significant at the 1% significance level, but the coefficients of non-resource-based cities are higher. It can be seen that without the constraints of resources, the industrial synergistic agglomeration of such cities has more advantages, so it is more conducive to coupling. Technological innovation . The overall regression coefficient of technological innovation on coupling is positive, but the effect is not significant. As an indispensable factor affecting coupling, technological innovation has not effectively promoted the synchronous improvement. Innovation can accelerate the development of the digital economy through technological improvement. The growths of digital economy help to quickly transform science and technology into the production process, promote the improvement of resource utilization, and reduce pollutant emissions in the production process. Therefore, this factor should promote the simultaneous development of the digital economy and ecological efficiency. The reason is that digital economy is mainly on the basis of extensional expansion, and the effect of "learning by doing" is not obvious. The breakthrough process of the innovation possibility boundary is relatively slow, and the large-scale expansion of digital capital has not only failed to realize the effective replacement of factor innovation and energy demand, but has also solidified the existing energy consumption pattern and aggravated the energy rebound effect. Therefore, there is an urgent need to bridge the digital divide and technological deficiencies. This also reflects that green technology research and the development of the digital economy have not fully matched in China, and key technologies and green innovation are seriously lacking. Environmental regulation . The overall regression coefficient of environmental regulation on coupling is negative, the P value is less than 0.01, it is significant at the 1% significance level, and there is a relatively obvious inhibitory effect. Furthermore, it also has a negative effect on the eastern and resource-based cities with developed industries and large energy consumption. This may be due to the fact that environmental regulations often cannot be adjusted in time with market changes. In the context of the digital economy, specific regulatory measures need to be further analyzed in detail, and the strength of regulation and spatial spillover effects must be accurately grasped. In addition, due to various constraints such as budget and investment use, the government may have certain limitations in the process of intervening in environmental governance. It can be seen that only appropriate environmental regulation policies can actually help companies or have obvious incentives, and can promote the benign interaction of economy and the ecosystem. Industrial upgrading . The overall regression coefficient of industrial upgrading on the coupling of digital economy and ecological efficiency is positive, and the P value is less than 0.01, which is significant at the 1% significance level. This reflects that new business formats derived from digital technologies. For example, 5G, cloud computing and other digital technology accelerate the transformation of traditional manufacturing into mid-to-high-end industries, thereby contributing to the industrial upgrading. The dividends brought by industrial upgrading have improved the ecological quality while benefiting economic growth. For the geographical location, the implementation of policies such as the rise of central China has continuously improved the market environment, expanded the positive external effects of the digital economy, and created more room for ecological quality improvement. The digital industry in the west is relatively scarce, and the development level of the digital economy is far behind that in the east. As a result, the green efficiency improvement effect of the digital economy has not yet been exerted. From the perspective of resource types, in view of the development characteristics of resource-based cities, it is necessary to get rid of resource dependence and realize industrial optimization, so the trend of industrial upgrading is more significant. The positive effect on the coupling of the digital economy and ecological efficiency is also more significant. However, the dividends of industrial digitalization have yet to be tapped. Government support . The overall regression coefficient of government support on the coupling is positive. In addition, the P value is less than 0.01, which is significant at the 1% significance level. Compared with other influencing factors, this factor has the most significant positive effect on coupling. This directly reflects the crucial role of the government's macro-control to ensure the high-quality development of China's economy. From the perspective of geographical location, the government in the east has relatively strong regulation and control, and the financial support is relatively good. This has led to the region being able to better enjoy the dividends of the digital economy, forming a stronger ecological advantage and promoting efficient interaction between systems. For non-resource-based cities, the government supports a diversified economy and the ecological pressure is relatively small. Therefore, the positive effect of government support on the coupling is more significant than that of resource-based cities. Economic foundation . The overall regression coefficient of economic basis for coupling is positive, with the P-value less than 0.01, significant at the 1% significance level. Regardless of geographic location and resource type, it passed the significance test. Specifically, the coefficient in the west is the highest, which shows that for relatively backward regions to a certain extent, the technological progress brought by the new economic form of the digital economy has a stronger enabling effect. Consequently, this situation is more favorable for the This situation is more favorable for of green total factor productivity. The digital economy in the east has developed earlier and has a higher level, which has enabled the release of the digital economy dividends to be more sufficient, and can make better use of the development of the digital economy to transform the traditional high-polluting production model. The economy of resource-based cities is mostly dominated by the mining and processing industries of natural resources such as coal and oil, and the high-tech industry is still in its initial stage. This makes the adjustment of the industrial structure rigid, coupled with the problems of high resource development intensity and low utilization efficiency, resulting in a more prominent contradiction between economic development and ecology. Therefore, the promotion effect of such cities is relatively limited compared to non-resource-based cities. Spatial quality . The overall regression coefficient of spatial quality for coupling is positive, with the P-value less than 0.05, significant at the 5% significance level. Compared with other influencing factors, the positive effect is weaker, which indicates that the current driving effect of space quality on the digital economy and ecological efficiency still needs to be improved. The reason is that both ecological protection and digital economy involve a large amount of infrastructure construction, such as solar power stations, high-speed railways, industrial Internet, etc. As a developing country, China's infrastructure construction still needs to be further expanded and improved. From the perspective of geographical location, except for the east, other locations are not significant, which is mainly due to the superior spatial quality of the east, which greatly promotes the synergy of the digital economy and ecological efficiency. From the perspective of resource types, the spatial quality of non-resource-based cities has a significant positive effect on coupling. For the specific reason, compared with resource-based cities, non-resource-based cities are more dominated by the tertiary industry, have superior spatial environment, and have a strong ability to gather talents, capital and other elements, thereby effectively improving the ability of the digital economy to maintain ecosystems. Discussion In view of the current lack of research on the coupling of digital economy and ecological efficiency and its internal mechanism, more attention is paid to the positive significance of digital economy for the improvement of ecological efficiency and environmental quality (Sturgeon, 2019; Langet al., 2020; Shahbaz et al., 2022 ). In terms of research objects, most of them are targeted at the national and other macro-regional levels, and there are few studies on the prefecture-level city scale. For example, Ulucak and Dankhan ( 2020 ) explored the relationship between digital technology and carbon emissions in the BRIC countries from 1990 to 2015. Higón et al. ( 2017 ) studied the relationship between digital economy and ecological environment in 142 countries. Therefore, based on relevant studies (Belitski et al., 2022; Xu et al., 2022 ), we carried out research on the interaction mechanism of the two systems at the prefecture-level city scale in China. First of all, we choose the research method scientifically and reasonably. To measure the digital economy system, we compared and drew on the Network Readiness Index (NRI) released by the World Economic Forum (WEF) since 2002, the detailed index of the development of the European Union's digital economy since 2014, and a series of internationally comparable indicators selected by the OECD. Combining the definition of the digital economy (Kim, 2017) with the actual development in China, we choose the currently generally recognized method, and mainly draw on the research of Zhao et al. ( 2020 ) to obtain the comprehensive development index of the digital economy. For the measurement of the ecological efficiency system, drawing on the research of Färe et al. ( 2001 ), we use the green total factor productivity measurement, which has also been recognized by many scholars (Shah et al., 2022 ; Zhang et al., 2020 ; Han and Chen, 2022 ). In terms of the selection of influencing factors, we draw lessons from relevant literature (Cai and Wang, 2022 ; Zhang et al., 2020 ; Williams, 2022; Kostakis et al., 2017), and screen them in the process of empirical evidence. In addition, for the measurement of the interaction between the two systems, we choose the commonly used coupling coordination model. Second, the study concludes that the interaction between digital economy and ecological efficiency in prefecture-level cities is enhanced in China, and there are obvious characteristics of spatial and temporal heterogeneity. This confirms that the digital economy has the characteristics of openness, time-space intersection and economic sharing, and has a significant spatial spillover effect on the ecological environment (Lange et al., 2020 ; Anagnostopoulos et al., 2021 ). The result is a confirmation and extension of previous research. Although there are few studies that combine the two systems, that is, the heterogeneity of the interaction of the digital economy and ecological efficiency, the combination of multiple literatures can still provide clues for our findings. On the one hand, for the single-system research, An and Yang ( 2020 ) pointed out the new development brought about by the internet reshaping the geographical pattern of China; Guo et al. ( 2022 ) emphasized the spatial-temporal characteristics of provincial digital economy in China; Seferlis et al. ( 2021 ) stressed that traditional energy consumption and pollution emissions are posing serious challenges; Zhang et al. ( 2020 ) analyzed the spatial-temporal heterogeneous characteristics of ecological efficiency, simultaneously its enhancement mechanism was explored. Combined with the spatial-temporal laws of each system, it can be speculated from the side that there must be heterogeneity in the interaction of the digital economy and ecological efficiency. On the other hand, similar studies can also corroborate our findings. Metcalfe's law and Moore's law of the digital economy determine whether the digital economy can provide intelligent, networked and digital technical support for the green transformation of the economy (Myovella et al., 2013; Truby, 2018 ). Sturgeon (2019) stressed that the digital economy can actively innovate new ecosystems in industry. Asongu et al. ( 2018 ) found that information and communication technology in Africa has an impact on carbon emissions. Jacob ( 2018 ) found that digital technology can contribute to the construction of low-carbon cities. Sareen and Haarstad ( 2021 ) found that digitalization plays an important role in environmental improvement and innovative development. Zhou and Yang ( 2018 ) confirmed that the coupling of national economic development and ecological environment is on the rise in general, but it is still at a low level, and there are uneven spatial distribution characteristics in various regions. The research of Zheng et al. ( 2021 ) pointed out that China's provincial green economy and digital economy are on the rise together, and there is a spatial imbalance pattern of high in the east and low in the central and western regions. He et al. ( 2022 ) confirmed that with the development of the digital economy, there is also an upward trend in ecological efficiency, and pointed out that the eastern region has advantages over the central and western regions. Liu et al. ( 2022 ) also pointed out that under the background of digital finance, the synergy between economic development and the ecological environment has been continuously enhanced in China, but there are spatial imbalance characteristics. Liang et al. ( 2021 ) also revealed that due to the spatial spillover and network effects of the digital economy, eastern China can drive the development and interaction of the central and western regions. Although these studies do not explicitly study the interaction between the digital economy and ecological efficiency, it is emphasized that digital economy can effectively improve green total factor productivity and is expected to meet the future challenges of economic growth. In addition, in China, with the continuous promotion of regional integration, more and more research can provide strong support in many aspects for the strengthening and heterogeneity of the interaction between the two systems. Finally, we found that each influencing factor has different effects on the system coupling, and there is also heterogeneity in the degree of effect on different geographical locations and resource types. In fact, in addition to the listed factors, there are also abundant researches about influencing factors of the coupling between economic development and the ecological environment (Erdmann and Hilty, 2010 ; Shaikh et al., 2020 ;Umar et al., 2020 ༛Zhou et al., 2022 ). We draw on relevant research and take into account the characteristics of the research system. FDI, population size, and other factors were put into the model for regression. Combining the influencing factors and regression results of the digital economy, through screening and comparison, we finally selected influencing factors such as industrial collaboration, technological innovation, environmental regulation, industrial upgrading, government support, economic foundation and spatial quality. At present, few scholars have analyzed the influencing factors of the coupling of digital economy and ecological efficiency, which highlights the value of our research. From the perspective of relevant research, for technological innovation, most scholars have pointed out the positive role of innovation and the digital economy (Shaikh et al., 2020 ; Wu and Yan, 2021 ). While promoting the development of digital transformation, technological innovation is conducive to the efficient distribution of energy (Murshed et al., 2020 ) and the healthy and stable energy industry (Litvinenko, 2020 ). It has an important role and has been confirmed (Xu et al., 2022 ). Scholars also pointed out the reasons for the weak interaction between economy and ecology of the resource-based cities, which was from the viewpoint of technical heterogeneity. However, the positive effect on the coupling of this study is not prominent. It can be seen that China's technological innovation has not fully produced real results. And the development of digital technology in China has not achieved an effective replacement of energy demand. There is an urgent need to unleash the potential of innovation and promote the synchronous leap of the system. In terms of environmental regulation, the government can accurately quantify the pollutant discharge and pollution control capacity of producers through the digital supervision platform, and effectively achieve joint management across departments. Shahbaz et al. ( 2022 ) stressed the positive impact of government regulation on renewable energy. Nizam et al. ( 2020 ) supported the digital economy to continuously alleviate global pollution by strengthening environmental resource management. Therefore, environmental regulation should have a positive impact on ecological efficiency (Cui et al., 2020 ). However, this study found that it has a negative effect on the coupling, which shows that in the interaction between the digital economy and the ecological efficiency system, it is urgent to strengthen the accuracy of environmental regulation and fully tap its positive effect. Regarding the industrial collaboration factor, Ali et al. ( 2018 ) emphasized the positive role of industrial synergy in energy internet, digital economy and ecological efficiency. Cai and Wang ( 2022 ) pointed out that industrial synergistic agglomeration will stimulate the digital economy. Moreover, it will also ensure the cleaning function and promote the improvement of ecological efficiency. For industrial upgrading, the linkage effect, spillover effect and diffusion effect of the digital economy drive the upgrading of the industrial structure. The optimization of the industrial structure will help the entire industrial system gradually evolve to green production (Seferlis and Varbanov, 2014; Shahbaz et al., 2022 ). This will further promote the development of digital economy and green economy (Vassileva et al., 2012; Heo and Lee, 2019). Ren and Du ( 2021 ) emphasized that the transformation of industrial structure will effectively improve ecological efficiency, while further releasing and promoting the development of the digital economy. For space quality aspects, Superior spatial quality will compress the space-time distance and increase the flow of factors to stimulate economic growth (Czuryk, 2021). In the digital era, poor spatial connectivity will inhibit economic development (Bowen and Morris, 2019 ; Philip and Williams, 2019 ). Amuso et al. ( 2020 ) also pointed out that the improvement of space quality can help the digital network effectively integrate resources and reduce carbon emissions. Fan and Xu ( 2021 ) also pointed out that the government should lay stress on the construction of information infrastructure in the central and western regions, provide financial and equipment support, and strive to bridge the digital divide. In terms of economic foundation, Falk and Hagsten ( 2021 ) stressed that differences in regional economic development will form a digital divide. the economic strength of each city is the basic condition of the digital economy and the key to improving ecological efficiency. Therefore, the positive effect of this factor on the coupling is undeniable. It is worth mentioning that, the study also has certain limitations. On an industry scale, it can be subsequently probed on the basis of three- or four-digit industry to replace the more macro two-digit industry. At the same time, the correlation among the subdivided industries can be further analyzed. In terms of influencing factors, the joint prevention and control of system, culture, environmental regulation, and economic globalization will all implicate the development trend of digital economy and ecological environmental protection. However, due to the difficulty of quantification and the limitation of data acquisition, this study did not conduct in-depth research, but this is also the direction that will be refined in the future. More, we will further optimize the research model in the future. Conclusions And Suggestions Conclusions This study explores the heterogeneous characteristics of the interaction between digital economy and ecological efficiency in prefecture-level cities in China from 2011 to 2020, and analyzes its internal mechanism. This has reference value for the precise implementation of policies to promote the systematic coordination of Chinese cities, and is also conducive to achieving a win-win situation for China's economy and environment. The specific conclusions are as follows. From 2011 to 2020, the coupling coordination degree of China's digital economy and ecological efficiency showed a linear upward trend as a whole, from a transitional state to a coordinated development. In terms of space, there is an obvious jump, and most cities tend to be synergistic in the later period. The absolute difference among cities fluctuated upwards, the relative difference was relatively stable, and the regional linkage was not good. Among them, the coupling of Guangzhou, Hangzhou and Xiamen is the best. This shows that there is a certain spatial-temporal heterogeneity. While there is a positive trend, more balanced interactions are urgently needed. In terms of geographical location, the coupling in the east of China has an obvious advantage, but the growth rate in the west is the most significant. Therefore, regional coordination needs to be promoted, and it is urgent to break down the regional barriers and promote the flow of factors. In terms of resource types, non-resource-based cities are more inclined to diversified development, the coupling of digital economy and ecological efficiency is obviously better than that of resource-based cities, and the interaction is obviously active. This further confirms the urgent need to seek more refined and efficient interactions for different cities according to local and category-specific conditions. Industry synergy, industrial upgrading, government support, economic foundation, and spatial quality all significantly have a positive effect on the coupling of digital economy and ecological efficiency, especially government support. However, technological innovation has not shown its due positive effect, showing a certain lag. It can be seen that China urgently needs to break through the boundary effect of innovation, strengthen independent research and development, make up for technical defects, and strengthen the compatibility of interaction with the two systems. The negative effect of environmental regulation reflects that the science and precision of regulation need to be improved. The positive role of government regulation and spatial quality in eastern and non-resource-based cities is more significant, which is a reflection of the effectiveness of their development strategies. The central region is actively accepting the industrial transfer from the east, so industrial upgrading has a relatively significant role in promoting coupling. The industrial synergy and economic foundation in the west have a significant positive effect, reflecting the short-term dividends brought by Western Rise Strategy. Furthermore, the positive effect of industrial upgrading on the coupling of resource-based cities that emphasize transformation is also relatively dominant. However, the intensity of environmental regulation in such cities still needs to be scientifically controlled, and the quality of space needs to be improved urgently. Suggestions First, formulate differentiated urban development policies and strengthen regional linkage effects. Governments should gradually implement digital economy and ecological development policies that are compatible with their local resource advantages. This requires the government to carry out reasonable macro-control and give full play to the effective role of fiscal intervention. For example, the east should promote the continued development of the digital economy to maintain its advantages in improving ecological efficiency. The central and western regions should receive preferential policy resources, seize the opportunities of the digital economy, and increase investment in green technology innovation and digital infrastructure. In addition, accurately grasp the development characteristics of resource-based cities, give full play to the enabling effect of the digital economy on traditional industries, and release the driving potential of technological innovation and spatial quality to the interaction. Second, release the positive effects of factors such as technological innovation, environmental regulation, industrial optimization, and space quality. While continuing to improve the construction of digital infrastructure, it is also necessary to improve the talent introduction system, strengthen the investment of science and technology in the field of environmental protection, and promote the simultaneous development of production, education and research. To achieve green transformation by transforming traditional industries, promoting emerging industries, promoting the driving force of some digital technology in industrial upgrading, then releasing the development of digital industries. In addition, to promote the organic integration of fiscal policies, administrative orders and market mechanisms, cities should make good use of environmental regulation tools according to actual conditions, strengthen joint prevention and control, and positively adjust the synergy between economy and ecology. Third, promote the deep integration of the digital economy and the ecological efficiency system, and seek synergistic interaction points. In the process of production and consumption, it is necessary to use digital technology to enrich the types of green consumption products, and use more high-quality digital smart products to satisfy and guide green consumption. In addition, the government should actively take the lead in promoting the digitalization of the industry. By strengthening the integration of front-end and back-end industries and the cooperation of upstream and downstream enterprises, the resource consumption on the production end and the pollution discharge on the consumer end should be reduced, thus the coordination of the two systems should be continuously promoted. Declarations Author contribution Ziyan Zheng: conceptualization, methodology, writing—original draft. Yingming Zhu: reviewing and supervision. Yi Wang: conceptualization, writing—reviewing, editing. Yaru Yang & Zijun Zhang: data curation. Funding This work was supported by the National Social Science Foundation of China [20BJL106], the National Natural Science Foundation of China [41901205]; Cultural Experts and Four Batches Talents Independently Selected Topic Project [ZXGZ[2018]86], and Postgraduate Research & Practice Innovation Program of Jiangsu Province [KYCX21_0357]. Data availability The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request. Ethical approval This research project has been approved by the Ethics Committee of Nanjing University of Science and Technology. Consent to participate Written informed consent for publication was obtained from all the authors. Consent for publication The authors confirm that the article described has not been published before; not considering publishing elsewhere; its publication has been approved by all the co-authors; Its publication has been approved (acquiesced or publicly approved) by the responsible authority of the institution where it works. The author agrees to publish in the following journals, and agrees to publish articles in the corresponding English journals of Environmental Science and Pollution Research. If the article is accepted for publication, the copyright of English articles will be transferred to Environmental Science and Pollution Research. The author declares that his contribution is original and that he has full rights to receive this grant. The author requests and assumes responsibility for publishing this material on behalf of any and all the co-authors. 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Economic Probl 11:8–17 Zhu BS (2020) Research on the Impact of Environmental Regulation on the Quality of Local Economic Growth.Southeast University,26–39 Supplementary Files renamedb1e11.docx Cite Share Download PDF Status: Published Journal Publication published 16 Jun, 2023 Read the published version in Environmental Science and Pollution Research → Version 1 posted Editorial decision: Major Revision 17 Mar, 2023 Reviewers agreed at journal 08 Feb, 2023 Reviewers invited by journal 08 Feb, 2023 Editor invited by journal 02 Feb, 2023 Editor assigned by journal 18 Jan, 2023 First submitted to journal 13 Jan, 2023 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-2476754","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":174420172,"identity":"84eec1c9-d1f5-4b2e-af4e-87d17b72ecd6","order_by":0,"name":"Ziyan 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system\u003c/p\u003e","description":"","filename":"Fig1.png","url":"https://assets-eu.researchsquare.com/files/rs-2476754/v1/b251239596b0a0839d3747e5.png"},{"id":32723879,"identity":"5dfefcb0-a2c9-4a73-ab87-05146742821e","added_by":"auto","created_at":"2023-02-09 20:46:49","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":57919,"visible":true,"origin":"","legend":"\u003cp\u003eTemporal characteristics of the coupling coordination degree of China's digital economy and ecological efficiency\u003c/p\u003e","description":"","filename":"Fig2.png","url":"https://assets-eu.researchsquare.com/files/rs-2476754/v1/996916f2b97eaf5c042649e1.png"},{"id":32723882,"identity":"e8be1089-930f-4324-a813-ce02bafedd6c","added_by":"auto","created_at":"2023-02-09 20:46:49","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":250240,"visible":true,"origin":"","legend":"\u003cp\u003eSpatial pattern of coupling coordination between China's digital economy and ecological efficiency\u003c/p\u003e","description":"","filename":"Fig3.png","url":"https://assets-eu.researchsquare.com/files/rs-2476754/v1/bdf1881b8ee5cfd581a56eff.png"},{"id":44733057,"identity":"712330c2-94b6-401a-9ca9-84eecbddc1e5","added_by":"auto","created_at":"2023-10-16 22:03:31","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":898070,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-2476754/v1/80d86a99-3371-4320-96cb-372fa541b8a5.pdf"},{"id":32723881,"identity":"dd3904ea-dfc8-4672-86c4-8f2257291809","added_by":"auto","created_at":"2023-02-09 20:46:49","extension":"docx","order_by":5,"title":"","display":"","copyAsset":false,"role":"supplement","size":28831,"visible":true,"origin":"","legend":"","description":"","filename":"renamedb1e11.docx","url":"https://assets-eu.researchsquare.com/files/rs-2476754/v1/cbfa9f369058a9b85f11f0d4.docx"}],"financialInterests":"","formattedTitle":"Spatial-temporal heterogeneity of the coupling between digital economy and ecological efficiency and its influencing factors in China","fulltext":[{"header":"Introduction","content":"\u003cp\u003eIn the context of dual carbon, in order to achieve high-quality development, China urgently needs to pay attention to the contradictions of \u0026quot;high input, high consumption, high pollution, low quality, low benefit, and low output.\u0026quot; Implementing the concept of ecological efficiency and pursuing a green economy has become the fundamental strategy to coordinate the conflict between environmental pollution and economic growth (Zhang\u0026nbsp;et al., 2022;\u0026nbsp;Ahmad and Wu, 2022). Simultaneously, the digital economy such as 5G, cloud computing and artificial intelligence, as a manifestation of the quality of economic development, has not only become a new driving force, but also a key support for improving ecological efficiency. Therefore, developing the digital economy and improving ecological efficiency has become a common choice for many countries to recover and develop after the epidemic. Then, exploring the heterogeneous law and influence mechanism of the interaction between digital economy and ecological efficiency is of great significance for giving play to the green value of the digital economy, and promoting the high-quality development of the economy.\u003c/p\u003e\n\u003cp\u003eTherefore, the more pressing question is, how is the interaction between China\u0026apos;s digital economy and ecological efficiency? Are there heterogeneity features? In addition, what is the interaction mechanism affecting the two systems? This study addresses the above questions. The contributions of this research are listed below. At the theoretical level, the existing research on ecological efficiency is mostly based on the analysis of the environment, industry, technology and other dimensions, while there are few researches on the prefecture-level city scale combined with the background of the digital economy. The existing literature also does not pay attention to the interaction between the two.\u0026nbsp;This study introduces an interactive framework from the perspective of digital background on the issue of ecological efficiency, which shows a new research perspective for the synergy between digital economy and ecological efficiency. At the same time, it enriches and expands the theoretical content of the synergistic interaction of digital economy and ecological efficiency. At the practical level, this study more comprehensively analyzes the internal mechanism of the coupling, which is from the aspects of industrial collaboration, technological innovation, environmental regulation, industrial upgrading, spatial quality, economic foundation, and government support. Heterogeneity analysis was carried out from the perspective of geographical location and resource type. This will help clarify the boundary conditions of the digital economy and ecological synergy, and provide a useful reference for the government to take targeted measures.\u0026nbsp;Therefore, this study attempts to explore the heterogeneity of the coupling between digital economy and ecological efficiency from 2011 to 2020 in China, and to analyze the internal mechanism that affects their synergy in multiple aspects. This provides useful experience support and decision-making reference for unleashing the potential of the digital economy, improving ecological efficiency, and achieving dual-carbon goals and high economic quality.\u003c/p\u003e\n\u003cp\u003eFor the rest of this manuscript, the second part \u0026ldquo;Literature review\u0026rdquo; summarizes the relevant literature; the third part \u0026ldquo;Methodology\u0026rdquo; specifically elaborates the mechanism of the coupling between digital economy and ecological efficiency, expounds specific models, influencing factors, and the data source; the fourth part \u0026ldquo;Results and discussion\u0026rdquo; reveals the spatial-temporal heterogeneity of the coupling, probes into the internal mechanism that affects the synergy of the systems, and then makes discussions; the fifth part \u0026ldquo;Conclusions and suggestions\u0026rdquo; concludes the research and puts forward corresponding suggestions.\u0026nbsp;\u003c/p\u003e"},{"header":"Literature Review ","content":"\u003cp\u003eRegarding the research on ecological efficiency, the Organization for Economic Cooperation and Development (OECD) believes that\u0026nbsp;ecological\u0026nbsp;efficiency refers to the efficiency with which ecological resources meet human needs. Its core is to achieve economic growth based on resource conservation and environmental improvement, and it has become an important engine for the country\u0026apos;s leading development (Borel-Saladin and Turok, 2013; Mustafa et al., 2021). Simultaneously, it is an effective indicator that can measure the coordination interaction of economy and environment systems from the perspective of energy conservation and emission reduction (Nkengfack and Fotio, 2019; He et al., 2022). It not only provides an intuitive effect for the new economic development model, but also provides a basis and guidance for policy adjustment.\u0026nbsp;In terms of research methods, it mainly includes the implementation of green national economic accounting, the construction of a comprehensive evaluation index system, and the calculation of green total factor production efficiency, etc., to comprehensively evaluate the ecological efficiency (Abdallah et al., 2015; Zhang et al., 2020; Liu et al., 2022). As far as research hotspots are concerned, in addition to exploring the laws of spatial-temporal heterogeneity and agglomeration of regional ecological efficiency, existing research mostly focuses on the impact of digital technology, environmental policies, and ecosystem services on ecological efficiency (Zhang et al., 2020; Han and Chen, 2022). For China, it emphasizes its basic concepts and principles, focusing on the discussion of the green economy brought about by efficiency improvements (Wei et al., 2020).\u003c/p\u003e\n\u003cp\u003eWith regard to the digital economy, scholars emphasize that the digital economy is a series of economic activities driven by digital technology to optimize economic structure and improve efficiency. The most important feature of the digital economy is that it can break the spatial- temporal boundaries of factor flows and enhance the breadth and depth of inter-regional economic activities. At present, the research is biased, and most of them focus on the conceptual and theoretical level. The definition, development trend and promotion policies of digital economy are discussed. In addition, there are also studies exploring the laws of economic development in the context of the digital economy (Watanabe et al., 2018;\u0026nbsp;Grybauskas\u0026nbsp;et al., 2022).\u0026nbsp;Overall, the literature on quantitative research is relatively weak and mostly at the national and provincial level. As attention to the digital economy continues to heat up, the digital economy is developing rapidly. The economic effects and high penetration of the digital economy, as well as the obvious spatiotemporal heterogeneity, are gradually being explored (Zhou and He, 2020).\u0026nbsp;In addition, some scholars have conducted in-depth discussions on the positive effects of the digital economy, such as industrial optimization (Liang et al., 2020), technological upgrading (Han et al., 2019), production efficiency (Watanabe et al., 2018), pattern optimization (An and Yang, 2020), high-quality economic development (Zhao et al., 2020;\u0026nbsp;Akbari and Hopkins, 2022), and other aspects.\u003c/p\u003e\n\u003cp\u003eGiven that the digital economy itself is an environment-friendly industry, it can effectively reduce resource and environmental input by improving production efficiency, promoting collaboration efficiency, and stimulating innovation efficiency. Ultimately, the ecological efficiency of the city will be improved (Jiang, 2021; liu et al., 2022). It is an inevitable choice for the economy to seek opportunities and motivation from ecological efficiency to achieve high-quality transformation and sustainable development (Rickard et al., 2017; Xu et al., 2022).\u0026nbsp;There are many studies on the relationship between economy and ecology, the earlier ones are theoretical studies, such as circular economy theory (Beckerman, 1992), environmental Kuznets curve (KFC) theory (Grossman and Krueger, 1995). However, there are few studies on the relationship between digital economy and ecological efficiency, and most of them focus on the theoretical level\u0026nbsp;(Fahmi and Sari, 2020).\u0026nbsp;In addition, current related research mostly emphasizes the positive effect of the digital economy in improving ecological quality, and the digital economy has gradually become a key driving force for improving ecological efficiency (Yu and Zhu, 2022; Han and Chen, 2022;\u0026nbsp;Dunlap and Laratte, 2022). Specifically, the studies involved can be grouped into four categories. First, by analyzing the technological enabling means of the digital economy to promote industrial transformation, improve the efficiency and quality of the supply system, and then improve ecological efficiency (Belitski et al., 2021). Second, explore the positive effect of the digital economy on saving resources and protecting the environment from the perspective of resource utilization efficiency (Barykin et al., 2020; Ma et al., 2022;\u0026nbsp;Wang et al., 2022). Third, analyze the digital economy from the perspective of technological innovation efficiency and production model improvement to change the extensive growth mode and achieve ecological quality improvement (Han et al., 2019). Fourth, discuss the positive effects of digital economy development on leading the formation of low-carbon life and consumption patterns based on a green lifestyle (Zhou and He, 2020;\u0026nbsp;Kovacikova\u0026nbsp;et al., 2022).\u0026nbsp;With the continuous progress and application of digital technology, empirical research on the positive effects of digital economy on the spatial spillover of ecological improvement has increased (Yuan et al., 2021; Zhao et al., 2022; Guo et al., 2022). At the same time, the mechanism of the digital economy on ecological efficiency is gradually being revealed (Jensen et al., 2021; Gai et al., 2022). It can be seen that the arrival of the digital economy era is not only the improvement of ecological efficiency led by technological innovation, but also the green development caused by power conversion, structural upgrade and efficiency change (Rysina, 2021; Liu et al., 2022).\u003c/p\u003e\n\u003cp\u003eTo sum up, in the context of China\u0026apos;s high-quality development, the pursuit of synergy between the digital economy and ecological efficiency can inject strong momentum into high-quality development (Cui et al., 2020). Scholars have conducted sufficient research on ecological efficiency and digital economy, and many scholars have explored the impact of digital economy development on ecosystems at the provincial level. However, few scholars study the cooperative interaction law of the two systems. Most importantly, existing research has not discussed in depth the ecological value permeating the digital economy at the prefecture-level city scale. That is, the mechanism that affects the coordinated development of the digital economy and ecological efficiency about cities has not thoroughly been analyzed. Therefore, this paper explores these issues in more depth.\u003c/p\u003e"},{"header":"Methodology","content":"\u003ch3\u003eMechanism analysis\u003c/h3\u003e\n\u003cp\u003eBased on the network effect theory, innovation theory, endogenous growth theory, green growth and other theoretical foundations, the law of coupling between digital economy and ecological efficiency system is analyzed. In addition, fully excavating the influencing factors of coupling and effectively promoting a virtuous cycle of the system is a catalyst and an important characterization for realizing regional high-quality coordinated development (Pouri and Hilty, 2021; Dunlap and Laratte, 2022; Yu and Zhu, 2022). The specific mechanism is shown in Figure 1.\u003c/p\u003e\n\u003cp\u003eThe digital economy works from both the supply side and the demand side to effectively maintain ecological functions and provide new opportunities for improving ecological efficiency (Wang et al., 2020; Cai and Wang, 2022; Petrov, 2019). On the one hand, the digital economy has created space and new channels for the free flow of factors. By reducing search, transaction and matching costs, etc., the distortion of factor allocation is optimized, thereby improving ecological efficiency. On the other hand, big data platforms can monitor production in real time and use aggregated data to accurately predict input and output. The government or enterprises can form a scientific monitoring system, realize intelligent upgrading, and promote the coordinated development and virtuous circle of digital economy and ecological environment (Brown \u0026amp; Mcgranahan, 2016).\u0026nbsp;In addition, the ecological environment provides a material basis for the sustainable development of the digital economy (Wang\u0026nbsp;et al., 2017). When the ecological environment is affected by economic activities, it will also have a feedback effect on economic development. Especially when the ecological damage is more serious, problems such as energy constraints and resource shortages will restrict economic development. Only a good ecological environment can provide a sustainable material source for economic activities (Vaz\u0026nbsp;et al., 2017). Therefore, in order to achieve sustainable economic development and improvement of ecological efficiency, it is urgent to promote the coordinated development of the digital economy and ecological efficiency, and realize the dialectical unity of the two.\u003c/p\u003e\n\u003cp\u003eIn the process, the coupling of digital economy and ecological efficiency is affected by multiple levels. To promote the synergy between the two, the following factors should be paid special attention. Industrial synergy strengthens the cleaning function of the producer service industry for the manufacturing industry, which is conducive to the formation of a green and resilient industrial chain. While stimulating the growth of the digital economy, this factor strengthens the input level and output capacity, and promotes the economy to move towards a low-pollution, low-energy-consuming growth model (Cai and Wang, 2022; Li et al., 2021).\u0026nbsp;Technological innovation continues to inject innovative impetus into the synergy of digital economy, which can penetrate into labor, capital, etc. through the continuous \"multiplier\" effect, ensuring efficient output and ecological quality. Environmental regulation will have a forcing effect and can become an accelerator for the digital economy and green transformation. Strengthening control in the process of input and output can effectively improve ecological efficiency to a certain extent (Petrov, 2019; Yu and Liu, 2021). Industrial upgrading is manifested as the birth of new industries and new formats. The \"structural dividend\" brought about by the upgrading of industrial results improves ecological efficiency while benefiting economic growth (Shen and Huang, 2020). As the key regulator of the digital economy and ecological efficiency, the government plans the economic direction and manages the ecological environment. Its macro-control, especially financial resources, has a strong impact on the interaction between the digital economy and ecological efficiency (Tang et al.,2022; Spence, 2021;\u0026nbsp;Shahbaz et al., 2022).\u003c/p\u003e\n\u003cp\u003eTherefore, the synergy of green economy and ecological efficiency is beneficial to the realization of green, low-carbon and sustainable growth of the digital economy. Thus, in China, the research on the heterogeneity of the coupling between digital economy and ecological efficiency and its internal mechanism will help to promote the harmonious coexistence of the economy and the ecosystem.\u003c/p\u003e\n\u003ch3\u003eSpecific model\u003c/h3\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eDigital economy\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCurrently, there is no unified standard to comprehensively measure the level of the digital economy. Drawing on the research of Zhao et al. (2020), this study evaluates the development of the digital economy, mainly from the two aspects of digital finance and Internet development. Specifically, the number of Internet users, the proportion of computer service and software practitioners, the total telecommunications business per capita, the per capita postal service, the number of mobile phone users, and the digital financial inclusion index are all selected as the indicator system for measuring the digital economy. After comparing with the principal component analysis method, the comprehensive evaluation index of digital economy is finally calculated by using the entropy method.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eEcological efficiency\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe core of ecological efficiency improvement is to achieve the greatest possible economic growth at the least resource and environmental cost, that is, the improvement of green growth efficiency. Green total factor productivity is a comprehensive economic efficiency that considers the cost of resources and the environment, an important indicator to measure the quality of economic growth, and the key to achieving a win-win situation for regional ecology and economy. Therefore, this study uses this indicator as a proxy variable of ecological efficiency. Specifically, based on the SBM model, the Malmquist-Luenberger (ML) index model for constructing undesired outputs is used to measure green total factor productivity (Zhang et al., 2020;\u0026nbsp;Shah et al., 2022), and Max DEA 8 software is used to measure this index. The specific input and output indicators are selected as shown in Table 1.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 1\u003c/strong\u003e The index system of ecological efficiency\u003c/p\u003e\n\u003cdiv align=\"center\"\u003e\n \u003ctable border=\"1\" cellpadding=\"0\" cellspacing=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"16.161616161616163%\"\u003e\n \u003cp\u003eIndicator type\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"19.19191919191919%\"\u003e\n \u003cp\u003eIndicator selection\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"64.64646464646465%\"\u003e\n \u003cp\u003eIndicator description\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" width=\"16.161616161616163%\"\u003e\n \u003cp\u003eInput\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"19.19191919191919%\"\u003e\n \u003cp\u003eLabor\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"64.64646464646465%\"\u003e\n \u003cp\u003eNumber of employees in the unit/person\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"22.89156626506024%\"\u003e\n \u003cp\u003eCapital\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"77.10843373493977%\"\u003e\n \u003cp\u003eCapital stock/yuan (estimated by perpetual inventory method, depreciation rate is 9.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"22.89156626506024%\"\u003e\n \u003cp\u003eEnergy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"77.10843373493977%\"\u003e\n \u003cp\u003eAnnual total electricity consumption/ kWh\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" width=\"16.161616161616163%\"\u003e\n \u003cp\u003eOutput\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"19.19191919191919%\"\u003e\n \u003cp\u003eExpected output\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"64.64646464646465%\"\u003e\n \u003cp\u003eGDP/yuan\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"22.89156626506024%\"\u003e\n \u003cp\u003eExpected output\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"77.10843373493977%\"\u003e\n \u003cp\u003eIndustrial wastewater discharge / ton\u003c/p\u003e\n \u003cp\u003eIndustrial sulfur dioxide emissions/ton\u003c/p\u003e\n \u003cp\u003eIndustrial solid waste discharge / ton\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eCoupling coordination degree\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe concept of coupling can reflect the dynamic relationship among systems. Drawing on related research, this study uses coupling coordination degree model to measure the coupling level (Zheng et al., 2022; Ma et al., 2012). The coupling degree is C, and the larger C is, the higher the coupling. The specific formula is as follows.\u003c/p\u003e\n\u003cp\u003e\u003cimg src=\"data:image/png;base64,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\"\u003e\u003c/p\u003e\n\u003cp\u003eAmong them, \u003cem\u003eU\u003csub\u003e1\u003c/sub\u003e\u003c/em\u003e and \u003cem\u003eU\u003csub\u003e2\u003c/sub\u003e\u003c/em\u003e represent the digital economy and ecological efficiency evaluation index respectively. The coordination degree model is constructed on the basis of the coupling model to reflect the coordination degree of the two systems. The formula is as follows:\u003c/p\u003e\n\u003cp\u003e\u003cimg 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\"\u003e\u003cbr\u003e\u003c/p\u003e\n\u003ch3\u003eInfluencing factors selection\u003c/h3\u003e\n\u003cp\u003eBy referring to relevant literature, the following influencing factors were finally selected.\u003c/p\u003e\n\u003cp\u003eIndustry collaboration (\u003cem\u003eCXT\u003c/em\u003e). Industrial synergy reflects the degree of integration of the manufacturing and service industries, permeates the power of the digital economy, and promotes the improvement of ecological environment functions. This study uses the aggregated data of the mining and manufacturing industries, as well as the data of the employees of the producer service industry, and draws on the revised E-G index method to measure the level of industrial collaboration through the industrial synergy agglomeration index. For specific industries, please refer to the literature (Chen et al., 2016).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTechnology Innovation (\u003cem\u003eJSC\u003c/em\u003e). It is a key manifestation of the digital economy and an important driving force for ecological improvement. Patents are the embodiment of science and technology, as well as the characterization of innovation. Therefore, this study uses the sum of the three types of patent grants (invention, utility model, and design) to measure the technological innovation of a city, reflecting whether technological development activities are active.\u003c/p\u003e\n\u003cp\u003eEnvironmental Regulation (\u003cem\u003eHJG\u003c/em\u003e). Appropriate intensity of environmental regulation stimulates digital innovation, drives industrial upgrading, and promotes high-quality ecological development. Considering the problem of data acquisition of prefecture-level cities, this study finally selected the indicators of sewage treatment rate, green space rate and domestic waste treatment rate in built-up areas, and calculated the intensity of environmental regulation based on the entropy weight TOPSIS (Zhu, 2020).\u003c/p\u003e\n\u003cp\u003eIndustrial upgrading (\u003cem\u003eGJH\u003c/em\u003e). Industrial optimization and upgrading are the guarantee for the transformation of the digital economy to green development, and is the foundation for the improvement of ecological quality\u0026nbsp;(Ren and Du, 2021). Referring to the method of Fu (2010), this study uses the industrial advanced index to measure industrial upgrading, which reflects the in-depth development level from low-level to high-level industries.\u003c/p\u003e\n\u003cp\u003eGovernment support (\u003cem\u003eZFL\u003c/em\u003e). The government plays a macro-control role in both the digital economy and ecological efficiency, of which the most important is fiscal support. This can effectively make up for the shortcomings of the market, promote the efficient allocation of resources, and improve ecological efficiency. Therefore, this study uses the proportion of the government's public fiscal expenditure to GDP to measure the government's macro-control level.\u003c/p\u003e\n\u003cp\u003eEconomic foundation (\u003cem\u003eJFZ\u003c/em\u003e). The economic foundation is the basic guarantee for the development of the digital economy and the basic condition for ecological improvement. Differences in economic foundations lead to differences in the development of the digital economy, which will also affect the improvement of ecological quality. This study selects the most typical indicator, that is, using GDP to measure the economic base.\u003c/p\u003e\n\u003cp\u003eSpatial Quality (\u003cem\u003eKJP\u003c/em\u003e). The quality of space affects the city's ability to attract investment and population, reflecting the basic platform support of space infrastructure for economic and ecological development. This study uses the number of employees in the transportation, warehousing and postal industries to measure spatial quality.\u003c/p\u003e\n\u003ch3\u003eData sources\u003c/h3\u003e\n\u003cp\u003eIn this study, the data of cities with serious missing data, such as Bijie and Tongren, were deleted. Considering that China's municipalities are provincial-level administrative units, they are significantly higher than ordinary prefecture-level cities in terms of politics, economy, and population. As a result, the development of the digital economy may be quite different from that of ordinary prefecture-level cities. In order to make the samples more comparable and the test conclusions more robust, prefecture-level administrative regions are uniformly selected as the research object. Therefore, the time range of this study is 2011-2020, and the final sample size is 2750, covering a total of 265 prefecture-level cities in China. The data mainly come from China City Statistical Yearbook, China Energy Statistical Yearbook, China Regional Economic Statistical Yearbook and statistical yearbooks of various cities. Among them, the data of general industrial solid waste and industrial sulfur dioxide are from the \"Environmental Statistical Yearbook\"; the industrial wastewater is from the provincial statistical yearbook; the carbon emission data is from the CEADs database; the number of patent authorizations is from the CNRDS database; the digital financial inclusion index is obtained from the Digital Finance Center of Peking University. In addition, some missing data in individual years were filled with the method of interpolation.\u003c/p\u003e"},{"header":"Results And Discussion","content":"\u003ch3\u003eTemporal heterogeneity of the coupling\u003c/h3\u003e\n\u003cp\u003eFrom 2011 to 2020, the coupling coordination degree of digital economy and ecological efficiency showed some fluctuations, but it was almost a straight upward trend, and the overall situation was floating around 0.55 in China (Fig. 2). The interaction manifests itself as the evolution from a transitional state on the verge of dissonance to a coordinated development. And finally entered a collaborative state in 2019. In terms of cities, Guangzhou, Hangzhou, and Xiamen have the best coupling, with average values of 0.75, 0.74, and 0.73, respectively. Zhengzhou, Xiamen, Pingliang and other cities showed a faster growth rate, higher than 55%. This shows that under the Belt and Road strategy, international cooperation in digital technology has been carried out, the construction of digital infrastructure along the route has been accelerated, and cooperation in the field of digital interconnection has continued to deepen. As a result, the digital transformation of the industry has been promoted, and the digital economy and ecological efficiency of these cities have strengthened the penetration and interaction. During the study period, the number of cities in dysregulation decreased from 21 to 2; the number of cities in synergy increased from 1 to 156; the number of cities in the transition period decreased from 205 to 9, showing a significant decrease; the number of cities in the adaptation period increased from 48 to 108, and the synergy has emerged. As far as the difference among cities is concerned, the absolute difference shows a fluctuating upward trend, while the relative difference fluctuates steadily around 0.10. This reflects that the differences in the development of digital economy and ecological efficiency among cities continue to widen, resulting in significant temporal heterogeneity in the interaction between the two systems. This also shows that although the coupling between the digital economy and ecological efficiency of prefecture-level cities in China is increasing, there is still a lot of room for improvement.\u003c/p\u003e\n\u003cp\u003eFrom the perspective of geographical location, the coupling of digital economy and ecological efficiency in eastern, central and western cities of China has shown an upward trend from 2010 to 2020. Both the absolute difference and the relative difference are relatively stable, floating around 0.04. Relatively speaking, the coupling in the east continues to occupy a significant advantage, with an average value of 0.57, and has entered the ranks of coordinated development after 2018. The cities in the central region increased from 0.45 in the early stage to 0.60 in the late stage. Although they gradually evolved from a transitional state to a synergistic edge, their growth rate was the lowest in the early stage, only 32.85%. The coupling fluctuations in the western region are the most frequent, but the late stage also shows coordinated development, and the growth rate in the late stage is the highest compared with the initial stage, reaching 37.05%.\u0026nbsp;This shows that with the in-depth development of policies such as the rise of the central region, the development of the western region, and the Belt and Road Initiative, the development of the center and west has gradually been influenced and helped by the eastern region. The coupling of digital economy and ecological efficiency in the three major regions of China has risen relatively synchronously, but the stable and coordinated development of each region has not yet been achieved.\u003c/p\u003e\n\u003cp\u003eIn terms of resource types, during the study period, the coupling of digital economy and ecological efficiency in non-resource-based cities has an advantage, and it is always better than resource-based cities, with an average value of 0.56. Non-resource-based cities entered a state of coordinated development in 2018, while resource-based cities entered into synergy at the end. In addition, the difference between the two types of cities is relatively small and stable, with the absolute difference floating around 0.03. It can be seen that non-resource-based cities lack traditional energy and are more inclined to seek new driving forces for digital development. The rapid development of the digital economy and the improvement of ecological efficiency are more easily reflected, resulting in more active interaction between the two than resource-based cities.\u003c/p\u003e\n\u003ch3\u003eSpatial heterogeneity\u0026nbsp;of the coupling\u003c/h3\u003e\n\u003cp\u003eFrom 2011 to 2020, the coupling of digital economy and ecological efficiency showed a clear spatial jump and linkage pattern. Fig. 3 shows that in the early stage, the coupling of most cities in China was dominated by transitional level, and the interaction between the two was on the verge of dysregulation. In detail, only Shenzhen was the first to step into the collaboration. Moreover, there are few cities where the coupling is in the adaptation period or barely coordinated, and the distribution is more fragmented. Cities at this coupling level are mostly on the east coast. Cities in the transition period are mostly distributed in clusters, and cities in dysregulation are mostly scattered in the central and western regions. The coupling in the east is obviously better than that in the center and the west. It can be seen that the spatial heterogeneity of coupling is obvious, and the development of the digital economy as a whole is not synchronized with the ecological efficiency. In the later stage, the coupling of digital economy and ecological efficiency in most cities has entered a coordinated level, and the number of cities at the coordinated level has increased significantly. In detail, cities in synergy are mostly distributed in bands, cities in adaptation are distributed in sheets, and cities in transition are distributed in points. Additionally, the diffusion path generally shows the characteristics of spreading from the developed areas in the east to the underdeveloped areas in the central and western regions, which reflects the driving effect of the east to a certain extent. Therefore, there is a certain spatial heterogeneity in the coupling between the digital economy and ecological efficiency during the study period. The significant coupling gap caused by the unequal development opportunities among regions still needs to be further filled, and the benign resonance pattern of the two systems needs to be further promoted.\u003c/p\u003e\n\u003ch3\u003eInfluencing factors\u003c/h3\u003e\n\u003ch4\u003e\u003cem\u003eModel building\u003c/em\u003e\u003c/h4\u003e\n\u003cp\u003eBased on all-round selection of influencing factors, we verified the heterogeneity of the effects of these factors on coupling. Specifically, the equation between the coupling of digital economy and ecological efficiency and its influencing factors is expressed as follows:\u003c/p\u003e\n\u003cp\u003e\u003cimg 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tUKbyhpikaDtOEkfqjNB3XNPZ2nGHf3DBBObEyK6LG86W0erFaP4avW02Z+MiW+TzwLIs+DbzTTbookeZTske9gO/0dMY/svhPmK6TCrCXQGQt3GmYVp1V8aar2ZJv+z08gJMcA0AsMwDMMwDMMwtzV3xNYjhmEYhmEYhmGywQMFhmEYhmEYhmFi8ECBYRiGYRiGYZgYPFBgGIZhGIZhGCYGDxQYhmEYhmEYhonBAwWGYRiGYRiGYWLwQIFhGIZhGIZhmBg8UGAYhmEYhmEYJgYPFBiGYRiGYRiGicEDBYZhGIZhGIZhYvBAgWEYhmEYhmGYGDxQYBiGYRiGYRgmBg8UGIZhGIZhGIaJwQMFhmEYhmEYhmFi8ECBYRiGYRiGYZgYPFBgGIZhGIZhGCYGDxQYhmEYhmEYhonBAwWGYRiGYRiGYWLwQIFhGIZhGIZhmBg8UGAYhmEYhmEYJgYPFBiGYRiGYRiGicEDBYZhGIZhGIZhYtzxA4Vut4tarYb5+XlUKpXU+9fW1pDL5bC2tjZyWZrNJubn55HL5TAzM4PFxUV0u92Rv2dcBEGA5eVlzMzMoNlsTlocZsoJggCVSgVzc3NsL9iq/4uLi8jlcpHrQRBgZmYGy8vLE5JsMLrdbugPcrkc5ufnx+I305iE/nZqmTHMtJOlv8aMhqkZKCwsLCCXy8X+xt2B+Oijj/D73/8em5ubY31PGmtrazhy5AjW19fR6XTgeR5WVlbw1VdfTVQuU7rdLq5evYqzZ8+i1+tNWhwEQYDFxUXMzc2FtlQsFtFut7X3q/Yn212lUon8ViwWtysbtzVXr17F22+/Dd/3Jy3KxGm32/jqq6+wsrIyaVFGxsLCAnbv3o2bN2+i0Wig1+vhvffeS31unJMxDMPsXGq12lT01+44xBTh+76wLEsAEKVSaWzv8Twv8u9GoyEAiHK5PLZ3puE4TkyunUi5XBYARKPRmJgM1WpVABCu64pWqyWE2LItx3GEZVnC9/3YM51OR9i2LQCI1dXV2O+lUilMs9PpjD0PaVSr1YF1PMyzw+D7fqyOTYO9TBOu64opc8sDUa/XBQBtXTN9tl6vj0GyKLeDz90p+L6v9a0Mk4Vp6K/J6Nq1242pWVEAgNnZWTzyyCMAgCeffHIs76jVagiCYCxpD8Pm5ia+//3vT1qMHc/a2hoOHz4M13Wxvr6OPXv2ANiyrVdffRW9Xg9nzpyJPZfP53Hz5k1YloWjR48mpv/iiy8in8+PTX5Tjh8/PpFnh+HkyZMTeS+z/Xz66acAtupdVg4dOgQhBA4dOjRqsSIEQXBbreBMO6VSCU888cTI022325EV4Ulu2a1UKuFWu7m5udt6VWya9D5J7oR2baoGCuOm3W5jcXFx0mIwY6Lb7eLZZ5+FZVk4depU7Pfvfe97AIBr167FfguCAL1eDwcPHtSmfeHCBQDAo48+OkKJB2NxcXHg7TrDPDsMtVqNO2XM1NDtdlEoFCYtxh1Bt9vF4uIiNjc3Bxo49qPdbuPAgQP46U9/CiEEfN9HrVbD+fPnR/oeExYXF3Hu3Dlsbm5CCIFCoYBnn3122+XYDqZJ75PkjmnXJrugEYeW3uWtCL7vi1KpJGzbFo1GQ9Tr9XCbiOnScb1eD7c10Z/rukKI6FJWq9UKZXBdN7J03ul0xOrqqnAcJ7bUVCqVwvRd1xWO4xjJJcsj/8mUy+Uwv7Zti3K5HNn+QktfpB/aJlMqlWK/NRoN4ThObBuNLD9t+yK9qDLRdhFZh+pvcvl1Oh3heV6YvmVZwvO8kW/hWV1d7bttjfKjyizEre0OuqXxTqcjABiX6bC0Wq2wjGzbFoVCIbQ3sk35j3Rdr9fD50heeftGv2eF0NuZqVz9kO2F/ug52V7S6rVqR2r+TKhWq2EeLMsSpVIpZoetVksUCoVQ1kKhENGTTn9JdaVerwvP88K65nle+G6d7nRbj6rVqigUChG7bTQaoS5Ij5ZlCcuytDbs+34kT/38TT/SfJGurOXy7oesQ8qXCWllqurP9/0wD2p7oGsf1Hzp/IdOpmHLTEc/vcryy9dN6rVsG+o9nU4n1HG5XBbValVYlmXkD8knq3WG/O2wW4xd142Vh659Hjdku7KfoDzejmyn3uV2jfoOOp+i89u09ViIaJ2UfTH1jUzeK/cH+7VrSc9Pw7blQZg6K9YNFNQORLlcDp1uUucuCZ2jp0peKBSE53mi0WiEDq5QKETkIEOUDYIGD51OR3Q6nbCjnoWkxpQGHWSgJJfspGWDdF03bIxc1438JuuOZPQ8T5RKJbG6uhoZRFAHTN67L0NO0GSgUCgUhG3bYR7onlHvVyU5k/ZF9xsokD5kx0KMqlEzxbIsUa1WhRBbMqsNt07HrVYrYkPUGVI7XElnAjzPCx2Z7ETl96bJ1Y+kfaUkT1q97nQ6wnGc8BqdOdHlJYlSqRSpS/K7VTl175EHJeQH1AkNGsQIsVUmcj1xXVeUSqXQ1nR1QB0oNBqNUE65o0vpkl3SuROSVbbjTqcjLMsShUIhLF+6L0sdNPFFBMk8CFnOa6WVqU5/cn5kGWW7T/IRcnuQxCjKLAnZHnV1T64jQpjXa9k2yLZJHrlOuq4b+nPT8qU6Rb5DCKFtX7Pi+36sXuo67NuB53mxeqDrTN8ObKfe6bwh2XSj0Qh9rGzDrVYr0uGn+yzLEp1OR7RarbBOkn+p1+vhBLKunbQsS5ue3L9IatdoYK36yp1qDztioCDErQZANk6a6c2i/H4DBbUjSIdfdfeqMzm6Z7OQZGy6jq/O2audb9/3w9GrTndC3OpYy6PcpPzpGgXTgQI1LuqzwzQSKuS8+umd9Knr8Msz8Ul/ckM3Lkj/cpnU6/XUgQI9J3c26D7dNfVZ27ZjslC+TeUyyVfSQEFXr2X7KJfLiUEITOq/riGjjr38Htu2Y+8h26JGR5Zb9VO6ukLPyuVAnUbVv2Spa3SvWia6iYykgaWp78zii4QYfKBAcpmsFJmWqRD99SdDEyO6+iAHRzBhmDLrB+lItVOSnTCp1zSIlAcX6mBZvkZ67XQ6xgfVaWCszqY2Go2hZljJJkkeWunYDj+tIk+a0Mw2TR7ebmyX3qkuJ/WL1D6K+n7yQfJ9uj4C2Sf5EfL3anpykBRC165RnVLrR1Lfdiew484o0D5zACM/VHr33XdH/p3P541Cfe7duxcrKyuo1WrhtWeeeWZoed58801YlhXb10mHbd9+++3YMw8++CCArUOEqn5k3QHA3NwcgNHrUWV9fT3UDcVABoC//OUvI3vH1atXASBMW8eVK1cAAH/9138dud7tdrG5uQnXdSG2Bs+RP9d1ASA8aD9OHnjgAQDACy+8EB66NznUuW/fPgghsGfPnjA07BtvvAEA+O677/o++8EHH8D3/VhoYqLZbA4slym6ei3bx7lz57CyshKRb//+/QCAjY2N1PTff/99AFt6ImZnZ3Hz5s3QNtfW1uD7fngAXr6vUCig1+vh4sWLA+XvkUceiaR76NAhOI6DXq+XGLLXFLn+qnUcAL755pvYNTWPaQziiwbhj3/8IwCzs0AmZZqVfD6PEydOhHuuCQqXnFVv/d5D6MqsH3v27AnDZ8uBOc6cOYPnn38+/LdJvaYgDkePHkW320WlUsGRI0cA6O3m4YcfDuU3PW9w4cIFOI4Ta2f27ds3VNtz5cqV0Df/1V/9FY4fP47Nzc1tD18dBAF838djjz2GSqWCH/zgB3j44Yfx8ccfT0Xgi1GzXXq/ePEier0eHnvsscj1Xbt2Rf7d7XaxsbGBw4cPR+z82LFjAIBLly5F7lfLRO33UbATavOIYrEIy7KwsbHRNyDORx99hF6vB9u2I/JQO/Xhhx+mZX3q2HEDhWnkH//xH3Hw4EEcPnwYc3NzqNVqWFpaGjrdzc1N2LYdu06N1U6KP7+2toa5uTlcuXIF77zzzsjT//LLLwGgb+QoavjVyBsfffQRAODxxx/XPrexsQHbtrUNYy6XG+mHX/L5POr1Oi5cuADbtrG4uIggCIzsiQ4NHjx4ED/60Y/geZ7RO69du5Y4SBJChA36oHKNgs3NTZTL5UQZ0zCJdEaReu67777Yb9RBIjsbBXRwPm0gNyy7d+8GgMg3WSg6iWlnb7t80TvvvAPXdY06WOOKXlcsFmHbNo4fPx7q6f333w870NPAyy+/DGArkhDx9ttv4+mnnw7/bVKviUqlEg7OTpw4MTI5gyDA5ubmSCbOVD7++OPQZ/u+j4MHD0b0sV38+c9/BrA18FlaWkK9Xsfbb799235Ecrv0/p//+Z8AohMBOj7//HMAQKPR0Nr5+vp6pvdSsBN1QALcmiz8+uuvE5+ndiSp3m1XmzlKeKAwAvL5PGq1GhqNBubm5nD48GHMz8+PJFTY7fBhkeXlZTz77LP43e9+h1OnTo1sVk6HOvtA1Go19Ho9FAqFWCfkD3/4Q+Kz5Ox10ZBoJlh+rtvtIpfLDTVLfOjQIXzxxRcol8uo1WqwbTt1hjQIAtx///0AgOvXr6NYLMZmSvpx/fr1scg1Sm7cuLEt7yFHP26ylM8wFItFuK6L48ePh53rV155BZZlhR1OE8bti2hl76c//elY32MCrSqcOXMGQRDgwoULU/WhxdnZWXieh7Nnz6LZbKJWq+HgwYOxgZ9JvZ6fn8e5c+fw0UcfYWlpSdtBGhRa6aUZ6FGyubkZ+t7Z2VkcPXo01Md28umnn0byR6uFoxxwTRPbrXfTCYFRf5yWBiCDMo1h+AeFBwojZN++fVhfX0e1WsXm5iZ+85vfDJUezeAldTrH4XxHTRAEeP311+F53lgHCGnQtwP++Z//OfYbzSCoS40A8NlnnwEAfvSjH8V+27NnT2xmjsLDDZvXfD6PpaUlfPHFF3AcB4cPH+478Dx58iR6vR5+9atfZX7X3r174fu+1tGvra1FHF5WuUaF4zio1Wrad50+fTr1eepA6eKa02CHZt4/+eSTxHSSBqKDQAMfnd2NmnK5DMuy4DgOcrkcgiDA5cuXjVcUtsMX0bauH/7wh0b3m5TpoNCqwhtvvIGTJ09GtvRMCzTIO3HiBN5880387Gc/i/xuUq9rtRo2Nzfx6quvjmWbzJUrV2BZ1sh9P+Upre4sLy+P/ZsGly5dwt69e/veQ9u6pmmwOQgmepe/rUB/g9THe+65B8CtFZskSJZ3331X+7tJ+yBDfoXafh39VjkeeughANCGiu12u9s6uTYqeKAwAtRvMxSLRTiOY7wcTx0xdU8oNU6qodP927UUTsuMcofRdMZct0Q3jo4ldfI++OCD2G/07YByuRxrsGh/o24PLQBcvnwZQHwZslgsIpfLYWFhIXLt7//+7wEgdJBZZ1lodpDI5/N49dVXAfSfMdHNXuj2GOugjxv+5Cc/icgbBAHeeustzM7ODizXqHjmmWfQ6/WwsLAQyWuz2TSyxR//+McAtj6YJ9tft9sNz6488cQTsCwLZ8+ejenzxo0bsG07bCBowKBu50mawdXZ/IULF+B53tj3MQdBgAMHDmB9fR03b94Ml+OzdN6y+iIaBGWp67///e9h23Z4ziYNkzIdhhMnTqDX62FlZQXPPffc0OmNGlpVoL3PaufFpF7rttJ9++23I5ORVjrovUS320Wz2Ry4Lfjwww9jPptmgO+66y4AW7Z67do1dDod/PKXvxzbNw02NjbwN3/zN5Frm5ubYUe33W7jlVdewY0bN0Z6Lm8SmOj9nnvuQafTiZzxG2SA9Ld/+7cAgH/5l3/payf5fB6u62JjYyP20bdKpZJ55ZYG3L/+9a9jv12/fj11S++jjz4Ky7Jw7NixiL/sdrt44YUXtuWs48gZ40HpzMjhBdWT7nRiXD6JTtEfbNs2ji5A91PINyFunXqXI2XIYUF1UWPkeymqD51yp5PwpjHeKSqJLh9qGEMKjydHvJBD2ukiZ9BvarhJXThR+WQ/yULXKJ58uVyORG2RY6lTOdG7KIINRUWgWMb4v+gDq9bYVTYAACAASURBVKursTCSg0aKoChVcvQCeldSaNN+uif7SipLXXhQx3GGCqNKYdjIzqls5UgNcjQH0qlsw2Tbsu2o3y2QnxXiVvQaKhc1QoOJXGn5IvkoWlKn04nZixD6ei2H9CQ7ofI2tReyBSo3+jaLLhqRGtpOjVokR9mq1+uiWq2Kcrkc5qdUKoX3k8yUZ/l7EOp3Wsj/ye8i3an6oHvlNHRhJ+WwgBSykcLRZolUYuKLSDbyLVlDV3ueF4YNNcGkTHX6E+KWr6rX6+E3Z1R0UbBMGLbMTEmK0EKk1WvZ3qle0j0UOluIaLtnWt+oHler1dDPE3IbMIi/V0Nuk39S22XZ/uR8jwo1LLUcglaNkNVoNLYtPKYalU39d79wpvSbDhO9y5BfHBTyOWSf9Xo9tE/HcUK7b7Vake80ua4rbNvWRihSbU7XP5LDx8s+W31W165RvqnekRxJ387ZCUzNQEH3MSgyZPWa67raj12YOAEqQGpMkj5CpbuW9EEe6jDJhmE6SNDlWTZu+i4DVQJd57Tf87rfdPmTGwj5j6CKY9t2WDnJ8MlpJMlB4dPoY0hC3OrUU1pyh33QMGu6D7upH8uSkSuzqjddflRUm6NBUdaPgMlQXHX544Dqh1rkTjPpU+50O46TOPjSPUvIH2ZyHCcWQjVNrjTkWOykK5N6LedbLl95cG6KXJfUD/KoeaX3J92ns2vqhMv3kz7VjwHp4nGrdTLJz2Wpv9RxTwoBbNp5MfFFSR9cM/HN9BGvrJ25fmXaz56oc9EvtGPWkKhJ7xzE55qghkRNkiepXgsR9+00YdevjTRBDlWreyfpfpDOO9W5fh/aU9MexLbSoG8oqR+CTOqA7/SBgoneZYYNESsPvKjNoe9qqR97lD+4psplWifl8lldXQ3rTb8PpqntGiF/w8q27ZF/N2o7mZqBAsMIITLPck4K3Vc36drtGDubGZwsnfFxQDO6OuhDRUwcWjGZZmg1caeifu3WBFpFSXtO7QjTJJmMPCs9CPRBOxP6DRRKpdJAK1fjIOnDgqZ6J6rV6tTkiRkOPqPATA3dbheffPLJjjjw9Yc//AGO48SumYZ2ZJjtgL6pkXRAeNeuXdqIXsxW6NGXXnpp0mIk0u12cfz48ak8P2FCu93WfpsjjT//+c9Gz7muGzmQqvveysGDB2PhsrPw8ccfj+Sg9l//9V/HDqNPAvomxG9/+9vYb6Z6J44fP54pqhozvfy/SQvAMMDWobcvv/xS66CmkWvXrmF+fh7tdhv/8R//gddeew3Xrl0LD1KdPn0ad999944Y9DDjgw5aX79+Hd1ud9sHkd999x16vR6eeuopPP/885GoTZ999hkuX768Y+rcuOl2u7j//vvR6/XCA5vTVn+bzSb2798Py7IwMzOD559/PnNHexpYW1vD//zP/+C1117L/Owf/vAHowOhjz/+ON555x08/fTTuHjxYiQYAcmwe/fugeskhfP9t3/7N6P7P/zwQ60fCIIAV65cwalTpwaSY5TMzs4mRuUx1TuwpVvHcXakbTIaJr2kwTA7EdrXqx76pn23O/XQEjM6Bj1HNWoajYYoFAqR8yUUSIC3yUWhPcVZz99sF7Tv/072Ma7rGm23ks9tqQfcRwHtcTexE9UP7MSyM9U73TsJX8eMh5wQBp81ZRiGYRiGYRjmjoLPKDAMwzAMwzAME4MHCgzDMAzDMAzDxOCBAsMwDMMwDMMwMXigwDAMwzAMwzBMDB4oMAzDMAzDMAwTgwcKDMMwDMMwDMPE4IECwzAMwzAMwzAxeKDAMAzDMAzDMEyMqRkoBEGASqWCubk5NJvNSYvDaAiCAMvLy5iZmRlpGVGaQRCMLM2dQLfbDfOey+UwPz+PtbU1AECz2cT8/DxyuRxmZmawuLiIxcXFgfV0p+o4K0EQhHpmppdut8vtxW1AEASYmZnB8vLypEUZCzutXzM/P4/5+flJixGytraGYrGIhYWFicnQr52+U5iagcLVq1dx7tw5+L4/aVEYDd1uF1evXsXZs2fR6/UmLc5twcLCAnbv3o2bN2+i0Wig1+vhvffew9raGo4cOYL19XV0Oh14noeVlRXu5I+ZIAhw/vx51Go1tvEp58yZM9xeTIhKpYJcLhf7q1QqkxZt6rh69SrefvttttMBaDab+PTTT3H27NmJypHUTt9RiCmiXC4LAKLRaExaFCaBYcvI87wRS7QzqdfrAoDwfT/2m+M4O1ZP1Wp1x9df13XFlLlGRgO3F5Oj0+mE9cR1XdHpdCK/+74vyuXyhKSbHLp8T6OdTmv56No9srFRkSXv/drpO4mpWVFgbn9qtRrPiv8fn376KQBgdnY29tvm5ia+//3vb7dII+H48eOTFoFhmDGTz+fx+OOPAwAef/xx5PP5yO8nT56chFgTZ6fkexrlDIIAKysrY39Plrz3a6fvJHigwGwL7XYbi4uLkxaDGSOLi4u8xM4wdzi1Wm1bOnzTxk7J9zTK2e12USgUxv6eacz7jmCQZQgA4R8tpzUajch1otFoCM/zhGVZQoitZTjLsoRlWWJ1dTWSrrpEp6ZJ6VarVVEoFMIlT8/zBABh27Z2ea/VaolCoRCmUSgUIvfREqr8DvWavPQ1rDw6qtWqcBxHABCWZYlSqRRbzk3LhxBbS2UkS6vVErZtC8uyhO/7fX9LSr/VavUtIyFEmGfLskL5Pc8L5a/X6+Fvqj593xelUkmrKzVdx3FEtVqN6W1Y3TcajVi+1aVGE1lkHdm2HcohL3OS/tS/pOtkY/305Pt+TDb5nn7PppW5qX7V+iLbSLVaDfXhOI5wXTfybKlUEgBEvV5PLStZtzp5s/gbIeLl6rpuWA93Cmk6abVaYR7lLSOyvvvZrA5avic7oDIslUrhO2W7UtPsdDpidXVVOI4jyuWyaLVaka0suqV+VUZK38Sm+/nJTqcjSqVSaCdUr1utVmgLtm3HfKEOnZ+Q9dyvLEzKqV6vR9oJz/MiujIpFzlPhUJh4C0o5LPS/Fu/9GX/ol7v5zNUdG1Lkhxyv0J+7zDtK/lF3fvkNrNer4f5MtleOuq+Tr/yUX2nEPG2I01+3/cj79e1ZbpnZP+l3kvllJb/NJ+TxTb7tdOmuu50OmG/rlwui2q1KizLEo7jaN85zQzcGuqctO/7oYOkf9MeL3JWtIeZnJXsfHWdUM/zIk6i1WqF99Fe7nq9HnZGZSMX4pZToE6C7/vhu8kB667J8hQKhUianU5HWJYlWq1WZnl0lEol4ThO6PApPbkimuSj1WqJ1dXViHOlxmZ1dTXxt0ajETo9eZBG8ssDFl0ZFQoFYdt2TH61Y6Y65k6nE3E8aiWTBxyyk5AbvWF1T/eqg1PbtjPJQniep70vad+qDvX+fnqiOifbBd3n+37fZ9PKPKt+dbbRarXCuiLEVsOnsx+dvaiQ/aplRfUzq7/pdDrCcZyw7snlNexAQR74q3Y/Skx1Qn65UCjEbNLUZmXkDqvrumEHg/JpWZYoFAphmlTGpH95cE4yNRqNMD+qz6X0KZ90n2pLg/hJ2a6pntTr9fC6fG8/yJ507240Gn3Lgt7Vr5xo0Kurr+R7TcqFBkKNRsNoUJiEbqAgl0Fauo1GI0xD7bCn+QxdWqQ7aq9ku1N9C+mJ7h22fW00Gon5ltvzcrkcdsjTfN44+jryfWqnV/adsp7kgUE/+amdlOs9vT/NtwuRfDbMNP9pPicp7/1IaqdNdC3ryXXdsJ80bNsyCQaWOOmAjq6w6Zrc4SSj1I345MZA7YiFgmscN82eyDLZth0b+fq+LwBEOsEkj2rQtm1HOo10r2popvKokMHpOn9yY2maD5LFtu3wmlxRkn5zXTc2Q95vxkidTVYbdl1jn9RhShogqnonPaj3Dqp7cmxqWTqOE3FCprJQw6ui63hmGSioz6i6V3VKeU8bhJuWual+de8ol8vaZ9VBocnqD3WUZFzX1V4z8Tc0i6yu3A3rzKlzJjfO6jtGRVadkE3IHXZTm9W9W20sqZFWV3DUhlW+pvp4dUWHyk61EWqEB/H3lEfVNqlTqspkcsC9XC7H3k15lOtoUln0+43yoFtRVdNPKheSRa0XkxooEDr503yGDp3fI72p5aKmN4r2NW2goPoDXRupY9R9nX7lo7PzfvLL5aZOWgixpZukdt/k3Wn5J91n9TnDDhSy6prKudPp7MiD0dt6RkE+8PS9730v8b5vv/0Wi4uLuPvuu/Haa68ZpQcAd999d+Tfa2tr8H0fe/bsiVyfnZ1FoVBAr9fDxYsXAQCHDh2CZVl45513IvdalgXf9yMxkN977z08/fTTmeXR8f777wMA9u3bF5Hv5s2bqNVqmfNBzM3NhfKoz6m/dbtdbGxs4PDhw5Fwd8eOHQMAXLp0qW8e1tfXQ1lrtVoYh/kvf/lLav51dLtdrKyswHGc2G+//OUvAdzSGzGI7i9evIher4fHHnsscv3jjz/GzZs3M8vywQcfwPf9WNhAYtRxtIMgwMbGRniokHjttdcghIiVu0zWMh9EvwDw0EMPYXNzE5VKBd1uFwDw5JNPxtKW7T+JpaWlsFyazSaKxSI2NjYSQ5mm+ZuVlRU88sgjsbzNzc2lypIE7YG9fPkyDh06pJVllGTVCdkE/XcUNvvggw8C2PJH+Xwe+XweN2/exNGjR8PvHRw5cgQA8M0338SeV21J1RWFIlRtRD3wP4ifVN+1a9curUwmnDt3DisrKxE97t+/HwCwsbERu18ti36/nTlzBgDwwAMPRO4rFouwLAsbGxuxQBFqudCzL7zwQnivbKPTgonP0PF3f/d3AID/+I//CK9ROdZqtTAtALhw4UJoT6NuX5OQfRA9a9pGjrKvMyg6+WV0ddtUN2kk5f+7774Lf8/ic4ZhEF0//PDDoZw78WD0VB5mfvHFF0dy4IROrN93332x36jgvvzyy/Ca53nY3NxEu90GsNVIPvPMM7AsK+wMkoMdVWGbRAHKmo+sfP755wCARqMBsbXKFPlbX19PTWNtbQ1zc3O4cuVKbLA1qDy2bcd+o8ZvFNGTTHSWRZZr167BdV2tDoUQRp3hLHz99dcDPzuKMjfh0KFDKJfLOHbsGO6//35UKhU88MADA+ui3W5jYWEBJ06cwM9//nO4rjtQOs1mE71eLzbIGpbjx4/D87yRNZAmDKOTcdpspVLBo48+CgA4ceLEwOmcPXvWKE/j9pNpbG5uolwuJ+pyGK5duwbg1kBG5pFHHgGQ7g/y+Tzq9TouXLgA27axuLiIIAiwtLQ0lGyjZlCfsWfPHjiOE+k7nDlzBqurq5HOW61Wwy9+8YvwnknbzSiYhjzs3r0bAPDVV1+F12hwtl2d41H5nH5Mg663m6kcKLz11ltwHAeHDx8OO+3DQAWbxnPPPQcAOH36NICtmeLnnnsOxWIx/ODV+fPn8fOf/3xomQbBNB+DIlfwLCwvL+PZZ5/F7373O5w6dWpknSRqHKcBU1muX78+ZklGy6BlnoWlpSX4vo9isRg2/oPU67W1NfzgBz8IP0Y3itnQGzduDJ0G0W634ft+bFZ5nF86HYVOxmGz8/PzOHfuHD766CMsLS1pO7hZyCLjuP1kP0ZpTzpogD8ohw4dwhdffIFyuYxarQbbtsPV4GliUJ/x0ksvodfrhXn65JNPcPToUdi2HYZufvfdd7U7AiZpN6NiknkoFotwXRfHjx8PJ89eeeUVWJaFl19+eezvH7XPSeN2sBdTpnKg8L3vfQ/vvPMOLMvCgQMHBp49phHuJ598kniPvO1kdnYWruuiVquh3W7Dsizk83kcPXoUAHD+/HlcunRppMu1NNLWfRKcnF3WfGSFlqTfffdd7e80cNIRBAFef/31kc6i3nvvvQC2vmqZxChmgkmvH3zwQey3IAjQbDYzybJ3797YNjVibW1t5N+QINnOnTun/b1fB2CYMh+E2dlZnDp1Cq1WCwAiM3qmvPjii3AcB8VicWh57rrrLgBb28xGBS2Dt1qtyEyy+o52u41cLhfZClEsFgfqsA2rk3HYbK1Ww+bmJl599dWRbLmi7Z+yvnSM20+m4ThObIsLMWx9onbis88+S7zHdPUnn89jaWkJX3zxRTghl6bbSTCIz3jiiSdgWRbeffdd1Gq1cFLvl7/8JXzfR61WC7diEZO2m1EwLXkol8uwLAuO4yCXyyEIAly+fHnsKwqj9jn9mBZdbycDDxRIEeoy06hmp/bs2YP/+q//Qq/XQ6FQGMiRkdM4e/ZsrMG7ceMGbNuOOdcjR46g1+vhqaeeClcY9uzZA9u2cezYsZFvVfjxj38MYKvBl/PY7XZx5cqVgfORhXw+D9d1sbGxgcXFxYgclUql735d3XL3sI0ODdjkmSGC7E03I5SVJ554AgDw+uuvx2arSqUS9u3bl0kW2kf7k5/8JNLxCoIAb731VsRZ0syjqisqX5N9lbOzs3AcB5ubm7GOCC3XJzFMmWehUqlEbHbPnj3wPC/yvQUalKWh+0bDoLZGdXpzc3PkM6o0COn3biFE2KB1u11cuHBhoBmwYXWSxWZN0S27f/vtt5nTIWgQ9Morr/S9b9x+Mo1nnnkGvV4PCwsLkfc3m82hV8Z/9rOfAQB+/etfx367fv06PM9LTaPZbEZsPZ/P49VXXwUQbcebzeZEBw4mPiOJfD4fntN59913w0k98vWHDx8OdUlM2m5GwTTkIQgCHDhwAOvr67h582a4hXU7tmGO2ucQunZ6GnS97Qx6CppOeFOc6Gq1KsrlcnhyvVQqiVarFZ5GB6KfwdaFwZNDeRIU2UIOH0onyeXwZkLowyxSxAz5+dXV1Uj4NRUKhSlD8upOrGeRRwfdR6HqKG6xLJ9pPuTwnqqs/X6jkHT4v1P7rusK27Zj0QrUMqLoBxR2j+I+k6yrq6uhfPRuChuWlKYQtyI/6cIBypE/htU9lSt9u4JikMvvMJVFiFv2SjLJ4fOITqcTRtZR5SN55Kga/fREUSWAW+HrKCyiybNpZZ5Fv3IUFLIFimBC+afwrXJUGZIhLa44RcMpl8thPHM5nGW1Ws3kb+RwgBT2j6IIUd5Mv8UhRDyMLuVXF16Yom5QiFqSQ7WVNEx0IofM1aVtYrO6vJINqBFEZF9FUXUoTc/zwrLXhYGW64Yc8UcOG9poNCJx9ilsovrufn6S6o1az8hOVJl0YSZ1OlFD4lIENdkeksoirZwo0osaylaum/3KRfVZdK8cUSYpPK0ur1SmFJJSfg9dT4uqJLdJlIaJz+gHla3qm6lu6Bhl+yrnW9aTzm/r/LwuzVH2dZLKR/adukhcsj518suhXCkaH7VJSd8ckpG/G1IqlSLhrlU9qfk39TlZbLNfO22qaznE/rgi320HQwV0pQ9IUCdLCBEaR6vVinzYRG6QdR9nUv/o2wlp95FT1T1PyHHUqdD6fTxHF4qNPiSiovswR5o8Se+UG0OdfGn5SJIl7TdC/oCI7qNvSXnS2QE1kLKDoFB+8iBIZx+qzuVOjO47F8PqXv24j64zkCaLjPxhKPXjZ0kfckmy9bSPB1G5yR9R0oXu7fdsUpln1a/cUZI7g3J9TwpHaxKSVO5Uk+OVPyiVxd8Qcpxw27ZFvV4Pw/1m6bDr0qMQx2o6at2jby4MwqA6UelnszrS6hjJQINu6uxT3U/6QF+SrPIzZEPUoVQ7IYP6yawy6VA/uCZ/vLFfWqbvWV1djdiXPChNKxfSC8kGIPY8dYD6dcpNPkYlx49P6iDp0qHraT4jDd17q9Vq34mjYdrXpHzryiMp3yb6GVVfR5VTl27Su5Lkoo61/P5+PkKFJq6o32Caf7KNNJ+TlHdT3avlnabrfu3OTiMnxJDhGBiGYUbAwsLCyKItTStBEMC27UgUnMXFRViW1TcUNMMwzDRTq9XwzTffhGc6ZdrtNp566qkdF+yD2WIqDzMzDHNnUavVwrjXtzNXr16Nhfqs1WpGceIZhmGmkSAIsLi4iB/+8Ifa33ft2oWDBw9us1TMqOAVBYZhJsrp06dx3333TeXHn0bN4uIivv/972P37t3YtWsX7r33Xti2Dd/38fXXX+Pbb7+9I/TAMMztQ7vdxg9+8APYto3nn38+EvXns88+w+XLl/Hb3/527BGJmPHAKwoMw0yUo0eP3jGdY8uycOzYMfzpT38Ko2o5jgPbtvHBBx/cMXpgGOb2Yc+ePWg0GnAcB2+88Qb279+P/fv34x/+4R8AgAcJOxxeUWAYhmEYhmEYJgavKDAMwzAMwzAME4MHCgzDMAzDMAzDxOCBAsMwDMMwDMMwMXigwDAMwzAMwzBMDB4oMAzDMAzDMAwTgwcKDMMwDMMwDMPE4IECwzAMwzAMwzAxRjpQoM94z8zMjDJZZkqZn5/H/Pz8pMVgRkAQBJiZmcHy8nLm50zrfBAEWF5exszMDJrN5qCiMkxfBmmHxuXLarUaFhYWsLCwMPK0R83a2hpyuRzW1taM7i0Wi9uSr0F9E3P70Gw2sbi4iFwuZ3Q9C91uF5VKBXNzc9wuJTCygUIQBDh//jxqtRp6vd6okmUYZkoJggBXr141qvPdbhdXr17F2bNn2T8wY2Oa2qG1tTV8+eWX2NjYmKgco6bZbOLTTz/F2bNnJy3KSKnVaigWi8jlcsjlckadxiAIwvvpjxkt7XYbX331FVZWVoyuZ+XMmTM4d+4cfN8fKp3bGjFiXNcVgyZbrVZFo9EYsUR3Fp7n7ah0mZ1PljpfLpcFAK7nzFgZph0aNQCE67qTFmModP7/dsiXDgDGefM8L7y/Xq9vg3R3Lkl1ehR1fZh2yfd9US6Xh3r/tDNVZxSOHz8+aRF2NLVaDUEQ7Jh0GYZhmOkmCIKhZ213GrZtY2NjA+12O/GeIAhw4cKF8N+HDh3aDtGYKePkyZOTFmHsTM1AYXFxkZd+hqDdbmNxcXHHpMswDMNMN91uF4VCYdJibDu//OUvAQCnT59OvOf8+fN4/vnnt0skZgqp1Wp3xCB6qIFCt9sND43lcjksLCyg2+3G7ltbW8P8/Hy4h29+fj5yYGphYSFU9v79+yP7A9OeNZGRDqrkcjnMzc1pK7/8npmZGSwuLkZm0bvdLk6fPo35+XlUKhW0220sLCyE+dbNuNdqtUiay8vLMf3o3kv3mL5zbW0NBw4cQK/Xw8bGRqQsSIZKpYJarYaZmZnw0J5afur7k9IFbh0i0h0YVNOdn59HrVaL6YYOw9H9VD7DHiiq1Wphec/Pz2NhYSGWZrvdjuxHLRaLkXtU3QVBEN4/NzcXzjTJ79LZv04XJvZbqVQi+17lQ4Pyb5VKBUB6PQmCIHJga3l5GblcLjwgKJeHTNb6R4eVsx4+1JWHOptHaZM+TA6fUn2R9w+r1+Q86/Yap8kmH+xst9uYm5vDzMxMWD8HtQGywYWFhVj9n5ubi6RBA/qZmRl0u93wPvmeNB9HyIeBSV61/pjkKa0emtTTfv4xSZakdigJnS+jg/dUX9bW1kJZRzVxkqU8ZPtL2hM/jA9Q2xq6/9FHH8Xm5iYAxN5H9PPfsl0CWx1v0iP5h263G+ZvZmamb1uhYtLGDsLTTz8Ny7KwsrKiLZNut4u3334bzz33XN901L5HpVKJyGdSb3VpqKS1Z1nbEyC9b9Kvb5EFVfakPI4SVaeffPJJZrkqlQoOHz4MADh27FhEf5PI01gZdM9Sp9MRjuMIx3GE7/ui0+lE9usRrVZLAAj3cPm+L2zbFpZlRdLT7REzfbYfjuMIz/NEp9MRnU5HFAoFAUBUq9XIuy3LCt/daDSEZVnCsizh+354jZ4tFArC8zzRaDTE6upqeE2mVCqFupHzJ+/1rFarkXsoLdobmfWdUPZVNhqNsExc1xWFQkHYth2WD/1blXF1dbVvuq1WS9Tr9VhZC7FlF5ZlRXROMpRKpfB5eheVT71eF/V6PdT7oLRaLWFZlmi1WqGOVbtqNBqRfPq+LxzHiewzlXUn657Ss21bVKtVUSqVIvdSHkkXjuNo32OyF1LWkUqpVAptyaSe1Ov18N2u6wrP84RlWcJ1XdFoNMJ3qeVsUv9oj2ipVAr/LMuK2bucJ7We27atrX+dTkcIsVU3HMcJbapUKhntS9WVrSyHWofIfsl+0mRrtVphfXQcR7iuG+qj0WgMZQNyuXieJxzHEeVyOfQJAESr1RK+74d1R7ZXuexMfBzJZ1lWRF7yGXSfSZ7S6qFJPU3zj7Isae1QEkm+rF6vh/n2PE+Uy+VIPVd9ZD9UmYUwLw+yx0KhENo+6VqWYVgfUK/XQ7tS91sn7QFP89+qXZZKpfAcIqW5uroqPM8T1Wo1ci/V+yTfJIRZGzsIlFdKT/bpxOrqang9ydZc19XaL/lzk3rreZ62LZXLyKQ9k/OT1p4IYdY36de3yIJq32SH5BtkfSbpOct7ye6p7lHeVP9jIhfpXq0zpnnaKQw8UKDOAFVoQjUWUqSsIDJY3TVdhy7t2STIGcsyUqMgVyp14CDErYZL7XjrHAdVSvU+OS/UAFPHhBoAuVEQQkQ6GVneKYS+QaLn5ffSO6mCq2mkDUBUWWU8zxO2bcfuJbuQdaJzWtQBHPSwa7lc1qYpp2fbdqwxITuQ7SVJ9+Qg1cNrqp7K5XLsPZSm6SFAauzVeua6bkxO0zom276cbpK9p6VLdiBfI/s2qeeu68bqH91HDth1XW0dMEGt84Rt2zFbrdfrEadvIpsQIhw8kj4pz8PaAN1LgySC6olcV9VyoP9m8XE00JGhd2XJU1o9TPvd1D+atkNp6HwZlbNczzudTqb6K0S8XmUpD+rE6CbQBqmr/XxAUqcnbaAgo9qK/LxcRqQD9fmkQ6VJ+e3Xxg4K5VXXJhDyBJtuoEBlqdqvrqOfVG8bjYa2LVXfZ9qe0b1p7UnWvomub2EKvUv2zerAR32/SpaBArUFqn1Ruaj+J00uXZ3JkqedhxHYmgAAIABJREFUwsADBZqJUOlXaL7vh6M5tbDSTp33ezYJ27ZTHbrOsRH0LnlVwcSRktH1Q57F0v3RO7I4734DhbRT+TSDoEvDdKBAjajOUVODJzs0XbrDRsUhvZbL5UgDSOkldRqFELHVpiTdmTZmpM+kPxOowZE7Fa1WK3HWbJg61q8D1C/dpDpPZS7rWpWBbCbpj+ShDqG6EmiKZVmxToluZr9QKERmzk1k66e7YW0gyQZJNnnGOKkcTH0cdS7S9GqSJ9N6mPZ7mn8cpB3S0W+gkFbP01Dvz9LmDCLDoD5gkIGCif8e9nndvSZt7KDo2nJZJ9VqNdaOqbI4jqNdGadBnjwASKu3SX+NRiNTe0ayp7Unw/ZNBqXT6YhyuRwO8k3tMEtdJ52o9KsX/eRK00FannYKA51RaDab6PV6ePzxx43upz2MBw8exI9+9CN4nmf8rmGeNTkcfe3aNQDArl27Yr898sgjAICvv/7a+J0AjCIEffrppwAAsTVYi/0tLS1leueg0N7bK1eu4J133hkqrc8//xzAVsQIlQcffBCAmW6G4dChQyiXyzh27Bjuv/9+VCoVPPDAA9i3bx+AW3q/7777Ys8+/PDDAIAvv/xyJLJsbm6iXC4nlrEJTzzxBCzLikQEO336NI4ePRq5b5h60o9h0qUy/+abbxLvIZtpNBpaHa2vrwMA/vEf/xEHDx7E4cOHMTc3h1qtlqmOeJ6Hzc3N8GxBs9nEM888A8uy8P777wO4ZZuzs7OZZOvHKGxARz6fh+M4Rt8KMPVxpn7OJE9p9dC0nvbzj1nboWkhS5uze/duAMBXX30V3kP7xMlO5evj8AHTyHZF4Xv55ZcBAG+88UZ47c0334z5X5XNzU1tO7hnzx4A5n0T13UT68C+ffsyt2cm7ckk+iaVSgWPPvooAODEiRMjT584e/YsXNc1vn8YubYrT9vBUIeZb9y4kXpPEAS4//77AQDXr19HsVjE3XffbZT+MM9mhToF280kw44uLy/j2Wefxe9+9zucOnUqdGLDQg3hpFhaWoLv+ygWi2FHRD0YS85w3JjUkX7k83l4ngff97G2toZut4sgCCJlNa56sp31T+4I6cjn86jVamg0Gpibm8Phw4cxPz9vfHCRDh1SIIP3338fzz33HIrFYnhg8fz58/j5z3+eWbY0hrWBJPL5fKb7R+njTPKUVg9N6qmJfxyXfseNSXkUi0W4rovjx4+HunjllVdgWVbYiQW2t67eSczOzsJ1XfR6PdRqNTSbTeTzeaO2kg6BD8P169eN7jNtz0zaE2K7+ibz8/M4d+4cPvroIywtLWkH0KPEVKfDyLXdeRo3Aw0U7rrrLgDAxx9/nHrvyZMn0ev18Ktf/Srze4Z5FrgVC1nXmaDICjQr89lnnyWmQ7NcplCausgm9N6HHnoIwFaINRWKKDBOgiDA66+/Ds/zRjZAuPfeewEAV69eTbxnu2b/ZmdncerUKbRaLQDAL37xCwC3Zuh0UQ6Ixx57bCQyOI6DWq2mtb9+YfdUqJP71ltv4cyZMzhy5Ejk92HrSRLDpksd7H4zOA888AAA4N1339X+rupp3759WF9fR7VaxebmJn7zm98YyUINfq1WQ7vdhmVZyOfz4Uza+fPncenSpUgs9Kyy6RiVDei4fv06HMdJvc/Ux1H9PXfunPYe8klZ8pRUD9N+N/GPWdqhaSJrm1Mul2FZFhzHQS6XQxAEuHz5cmRFYVw+YFoxaWNHBc2+Hz9+HCdOnDD63hOtJiR9h8FkVnvv3r3wfV8bBXBtbQ1BEAzUnqW1J9vZN6nVatjc3MSrr76aeeJjECzLgu/7qRNMw8i13XnaDgYaKOzZswe2bWNzczPVaHSj0n5bEUb1LIAw/vMLL7wQud5sNsN0fvaznwEAfv3rX8eev379+kDLtz/+8Y8BAC+++GLEILvdLq5cuQIAePTRR2FZFo4dOxZpXLvdLl544YVwCXpc6LYZDBtWTp19kaFO49NPPz3UO9KgUKbEnj17whkU4NbS69mzZ2P2dePGDdi2nXlgmMQzzzyDXq8XC5/bbDb7fshHZXZ2Fp7nYWNjA+fOnUOxWIz8Pmw9SWLYdN999104jtN3IJrP5+G6LjY2NmKhLyuVSjgrqoakLBaLcBwn07dXjhw5gl6vh6eeeipsLMmXHTt2LDaINZWtH6OyAVXvQRDA93289NJLqc+a+rjZ2Vk4joPNzc1Yh5+2BpnmKa0epv1u4h+ztEPTRJY2JwgCHDhwAOvr67h582a45U2tU+PyAdOKSRtL/x423Pa+fftCX3P9+nWj9oG+r6DWIyontXOu48knnwQA/OQnP4nkIQgCvPXWW5idnR2oPUtrT7azb6Lb5vvtt9+OLH0Vyusrr7zS975h5NruPG0Lgx5ukA+8UOg4ijKE/ztc02g0ItE5KKSWHN5NDt9HaVWr1TD0pMmzSXQ6nfAQieu6YbSOpAgiahgy9T5d+DX5HfLhNDo0Y9u2KJfLolQqCdu2I/fQwSK6z3VdYVlWJF9Z3knpkK7k5ylUl5wG/u8wJOmbZKbQh5S2Lt2kqDYUeUIX+k8+QJUUzYVkkA9n0cEwNUqIDoqmQu+mMIFyxByyXTX8mxyuka6R7mR0h8TkKBNqGEkqYwqXp4s6kUa/Q1Mm9UQO0aZLg9KXI/eY1j9Kt1QqRUJUqvoU4tbBMzW0I9kSHU5VAxFQhC41sIAaeSoNCt0rQ+Wsi9hhIpusOzWNYW2A0pbtn8Ifyoez5TCmugN5pj6ODlvSveVyOQylmSVPafXQpJ6a+EfTdqgfSb6MbFU9+KnWk37o6lWW8pBDWlJEKioXWa5R+ICkkMGkh3q9HtZx0ruaL9V/J7VVpEc1v7ow00k6NGljde2JSXmpNqM7BCzfr/NFahhbqrdyXUqrt3KoX7IB9V7T9kwnd1IfKkvfRO1bkC3qwsqqyLJTxDnKo+d5YRpJdTTpehLUP5HrSrVajYRCplC/JnLJkZ/oPtNndxJDhQ2QY03bti3q9XrYoMtRTUhJjuNE4larIbmoYsnGYfJsP2TnKMf3V1ldXQ3zoruP5JD/ZCchN1aEHE++UChoDVmObW3bdsShZX2nHOO/1Wppn5epVqthp550ruuUq+nqZFDD1Mmx3qnCEORg0vJFnTHLsrShLHXIlZLKMqljLHd21PJJ0p1OxqT8CCEinWZ6T9YQcoTaqBIm9SRJt6QzXV6z1F05skNSPesnQ6vVitTTUqkU6xTI5UX+JitqqFwhbtlrEv1k61f2xDA2IDdElH9VhjQ/RKT5ODm/sk/SpZWWp7R6aFpP+/lH+Z60dihNvya+yKSsZdLuNykP6mgnRZqiOjSsD1D9nVo3qZ2oVqvG/lvu5Mp2mcX/J/kmIq2NpU6ria/Q6VdGFw2w3zP0vReST61LpvVW9q3y4FomrT3TkdSeEFn7JkShUNB+EyQJKiP6NhF15vv1N2hSwER/KnLfiPwOTVzoBt9JchHy9yTUSba0Z3cKOSGGCL3BMNvI3Nyc8UEkhrkdaDab2L9/P8rl8rZFQmOmh1qthm+++UYbZafdbuOpp55in8hMJQsLCzh16lQsOhez8xgq6hHDbBenT5/GW2+9NWkxGIZhtoUgCLC4uIgf/vCH2t937dqFgwcPbrNUDJNOu93G3r17eZBwm/D/Ji0Aw/QjCAKcOXMGTz755MgOGTPMToEi4/SLasLcnnz33Xfh4fvnn38+Er3ms88+w+XLl/Hb3/52ghIyTBwK9PDaa69NWhRmRPDWI4ZhmClkYWEBGxsbkWvsru8sms0m/v3f/x0XLlwIP67nOA5+8Ytf4Omnn75twi8yDDO98ECBYRiGYRiGYZgYfEaBYRiGYRiGYZgYPFBgGIZhGIZhGCYGDxQYhmEYhmEYhonBAwWGYRiGYRiGYWLwQIFhGIZhGIZhmBg8UGAYhmEYhmEYJgYPFBiGYRiGYRiGiTH1A4Vms4lisYiFhYVJizIxut0uKpUK5ubm0Gw2U+9fXl7GzMwMgiAIrwVBEF43SWNa6Ha7OH36NObm5lCpVMb2Hp3Oms0mFhcXkcvlIvcGQYCZmRksLy+PTR4mzvz8PObn5yctRma63S5qtRrm5+fHasPbTRAEWFxcxMzMzKRFuS1I8nXT5Lt3Unuctd00SW872qJxs9N90TD1gX3WYEz1QKHZbOKDDz7A2bNnJy3KRLl48SLOnTsH3/cHer7b7eLq1as4e/Zs+HXPncLFixdx+fLlgfM+KO12G1999RVWVla29b3M9JPL5Yz/KpUKPvroI/z+97/H5ubmpEUfGUEQ4Pz586jVajvOp0wrOl83Tb57p7XHw7abuvQm0RaNklqttqN90TD1gX3WEIgdAADhuu6kxRgrnuf1/b1cLgsAotFoDPyOYdNIk3FcNBoNAUCUy+XUe0cto+u6YodUE2absG1btFqt8N9kn7KP6nQ6wvO80Gaz2PC4qFarQ/kPHXdS/fB9f+zll2Qno/D/o2K72uNR6HvUepuGejwst0MehinXYXzWOHzoTmCqVxTuFGq1WmTLyzTCMjLMFidOnMCePXv63pPP53Hq1Cncfffd2yRVOsePH5+0CDuakydPTlqEOwrWNzNt3Kk+lAcKE6bdbmNxcXHSYvSFZWSYWxSLReN7jx49OkZJzFlcXNzRWyYmTa1W422I2wjrm5k27mQfOtBAQd6DS4dJms1m5DpBB0Lp8EilUsHMzAxmZmZw+vTpWNrtdhvFYjFMJ+nAaLfbDdPN5XKYn5/H2tpaJB36vdvtYmFhIUxP3UOsyi7nq1KpRO4l6JBULpdLPHxGh6jW1tbCe+XO7NraGg4cOIBer4eNjQ3kcjmjQ2K1Wi1Mr1gsotvtJr47DVWPMzMzWFxcDNNMk1Etr2KxiHa7HckjHX5rt9uYm5uLHBqW9ah73oR+MqblbxCd1Wq12IG+rHYeBEFEb+pfUj4XFxexsLAQ5ovypNqffIiPbF6uS7py0+VdPhBItq7LT7/6QO+bn58Pfy8Wi5F70n5X9Uv5NKlnw+h8HLTb7dAfLSwsaFfB0vSpSzNJfwsLC2Gna//+/eF7Kd+yHcvX1XeqdYnsUPesmq7sR/v5uLW1tTAfal1V05flk3+T7XgUfrpSqeDw4cMAgGPHjoXvztIOqrIM6ut0+R1E17Kede0nYdoeq/kjXcv20W63w0OodD/ZklzOSfqmNPr5iX70azdV+bOWT5o+B/FVav9G9c9pfSCdbDMzM3j//ff75iUIgvDQtiortePjzHtanyILaT7LNA86H0rlYVqXdjSD7lkqFAqxPWK+7wvLssL9X77vi3q9LgAIAKJUKoV7vBzHEQAie31brZawLEt4nic6nY7wfT+8T93/6ziOWF1dDd9D9zUajfC9JEuhUBCe54X78lZXVwUA4ThORHbbtmMyCSFEqVSK7H33PC+UkfYiQ9rzV6/Xw7Ron3Kj0QjvI7kJNX86aE8evbtcLofvcBwnlEV+t7qXTrevr1AoCNu2he/7kXtMZGy1WsK27TC9RqMhLMsSlmWJTqcjWq1WRNeu64b7AxuNRvib/DyVl4zpnkqdjGn5S9OZup+x0WiEadC7stp5p9MRlmWJQqEQlhvdp+pd1jW9g/RYKpVEqVQK30vP1uv1SL3xPE9YlhXKS/rU1Z96vR55r+M4EVunel+tVsN70uqDEEJYlhU+02g0hG3bxr/LeZfLIks9G0TnpujOKCTdQ75Itn/V3k30qZKmX13db7VaWrnJpuTnSV+O4wjf9yNyUZnI13S6KJVKsbzKVKvVMH0hRKgfSku2v1KpFHvecZxMdpnFfpJ8kEk7KOdlUF+nlt8wuqZyp3dQ22dZVuw+k/ZYiC0/qSs7amN93w/tksqPdC6302l6SLNzFZN2U5Z3kPIx0WcWWyP7kf0zPUv6TesDEdVqNZI+tdH9/Em9Xo/UM9d1RblcDtO3bTvU26jzntanUMu133kBE59lmoekd5o+u9MZeKCQVFC6gyJ0TS5oavhlY6XOpIzv+zHHVC6XY4dWdY01vZc6aXJnzXGcWGFSpZIbG0qHZCfnpKIaH+lH7nh1Oh2tk80yUFA7NZRH+XpS2eiuu64bc4Y6B5nUOKi6onfI5apzLnSvWgau68auDTNQMM1fFnvu9y4TO1cbJSGSO22691qWFbFleoesN9VeyFEKsXUYV60/VM9kh0zlo8uP3PCk1QcqPzUd9aBv0u+ErixM69kwOk8jy0BB7eCqfsjUv+jS7qe/JPvWya2rb6VSKWYLQoiwA0B0Oh1h27Y2D67rxiZh5Ocsywo7QvIzsty69oCel6+N2k9nPWSs2uqwvk73nkF1Te+Qf6f01TbSpD2mdlMtO90Al/QitxvywIrer9ODqZ+QMW03hykfU32a2hpNBMnQ4F1uO9P6QKRXVT9UXqYDLBl1cmUceTfpU5gMFEx9VtY8yO80fXans61nFPL5fPj/3/ve9yK/tdttbG5u4vHHH49cn52djaVz7tw5rKysRJZe9+/fDwDY2NiI3U8HD+UDiL/4xS/Q6/UiS0Qk07vvvhuRa3Z2NpT9gw8+gO/7idsW1OVBOZ9y/gflwQcfjPy7XC4DAC5fvjxQeuvr66jVagBuxVcGgL/85S99n+t2u9jY2MDhw4cjejh27BgA4NKlS5H75+bmwvxTOSwtLeHmzZsAbsXn3tjYGGnoskHzNwz97BwAvvnmm9i1tMOxMo888kjk/kOHDsFxHPR6vdgSLdkL2fDa2hp834+9b3Z2FoVCAb1eDxcvXgQAvP3223jkkUci+Tl06BCEEOHee5P68MADDwAAXnjhhXCbzaFDh8J70n43Ia2eDavzUaEebs7n8xF7z+pfgNHoL42VlZWYLQBb9Vomn8/jxIkT8H0/rHey3Ek6/+ijj9Dr9WDbdiTf5M8//PBDAFt26nkeNjY2Ilu2zp8/jyNHjoT/nrSfVhmHrxtU1/v27YMQAnv27Am3uLzxxhsAgO+++w5Atvb4zTffhGVZsd/IR7z99tuxZ+RzPlSmAPDHP/4xMb/D2HlauzlM+ZjoU6afrQVBgI2NjZjeX3vttfAdgFkf6OLFi+j1enjsscciae3atSs1T8TPfvazyL9fffVVALf0Nsq8Z+1TpGHqs7LmYVTP7iSm5jAzGbdq1Do2NzdRLpchtlZEYn8mPP300wCA9957L7z23nvvoVwuRxqh06dPRw4kXrt2Da7rJr573759xnkeBeQ4hun40p7BK1eu4J133jF65vPPPwcANBoNrR7W19eN0qH92idOnMDPf/5zuK47cD6SGCR/42T37t0AgK+++iq8RvsmdQ2xCQcPHgSQ7pw+/fRTAMB9990X++3hhx8GAHz55ZcAYHRwy6Q+5PN51Ot1XLhwAbZtY3FxEUEQYGlpCQBSfx8F49D5OBjEv4xbf81mE71eL9Z5SaJYLMK2bRw/fjzU8fvvvx/pyKuQXSblW84LdV7kqDjvvPNOpPM5bX4aGI+vG0TXwK292wcPHsSPfvSjsKNOZG2PbduOXae2ycSP/M3f/A0A/YCeGKWd69rNYconTZ+mfP3110b3mfSB/vM//xMARmrrjz76KICo3kaV91H1KYDsPmuYPIwq/9PM1AwUCLkh78eNGzeGek8+n4fneTh79iyCIEC73cbDDz8cDiBOnjyJbreLIAhiMzPXr18f6t3TxPLyMp599ln87ne/w6lTpzLPspqWl461tTX84Ac/wJEjR7C+vj7yWVBg+PyNg2KxCNd1cfz48XBA+sorr8CyLLz88ssDpZk1DCd1zEaBSX04dOgQvvjiC5TLZdRqNdi2HZkFTft9WMah83ExiH8Zt/6AbD6XZrrPnDmDIAhw4cIFo2hRJuGN9+3bB8dxsLKygm63i7W1NTzzzDOx+6bJT4/T12XVdRAEuP/++wFs6ahYLCb6D1P/PuwHvHQrrzrGZefDlE8WfY4S0/o4ypDhuhWQUed9mD6FiomOhsnDpMp+u5magQIp909/+lPqvY7joFaraU+v6yKyJEEzU+fPn8fp06fx3HPPhVswarVabDkbAPbu3Qvf97VbANbW1rY9jj+976c//elAz77++uvwPC9zB5qWgeVtWjIm5fDiiy/CcZxM4SazMEz+xk25XIZlWXAcB7lcDkEQ4PLlywPPbpNDpHJJgmbWP/nkk8R7aBbRtm1sbGxo6xk1zlnqQz6fx9LSEr744gs4joPDhw9H0k77fVhGrfNxMIx/GZf+7rrrLgDAxx9/bPwMzXS/8cYbOHnyJJ5//vm+9z/00EMAtnyxSrfbjXUGX3rpJQDAmTNn8NZbb4UTPMS0+elx+rqsuj558iR6vR5+9atfJd6TpT2m1YSkyDQmM/PUOSQ76Mco7FxtN4cpHxN9mnLvvfcC2NpapIPqgUkf6J577gEA/PnPfx5aLoL0tnfvXgCjzfso+hREFp81TB5Gmf9pZuCBAnUm1KX8QWdxnnjiCQBb+8rSQmE988wz6PV6sdCCzWYzUxitffv2wbbtcA8ljZb/9m//Fr1eD//8z/8cykU8+eSTAICf/OQnkUYoCAK89dZb297pOH/+PCzLislpgm6Z09Th5vN5uK6LjY2NWLjRSqViNKrWLUmPsmM4TP7GSRAEOHDgANbX13Hz5s1wWdV0MKPLw4ULF+B5Xur+6ieeeAKWZYUraTI3btyAbdvhUnWhUACwtSdY5v9n7wxf3DrO/f8V3LdtelZ91ZuGoqO8CM1lQ3Nkl147kEAsxS9KS5IrOZRgaIir9SVw22S31bqEEMc3Ut2+CI131yQQym2k/dmleVHJlg01RKLUzrpI1CYF7xEhNX2lYzXJHzC/F+4zHs2ZozNHOpJ27fnAkvjonJlnnnmeZ2aOZh61222+RUDHH9rt9tBEL5lM8r2uN2/eDP08DqLonL5hnAfjxJdJ9JfNZn0x+/r160P/XlxchG3b2NraivT29vjx4xgMBlhbW8MPf/jDkffu3bsXlmVhZWVlaELgeR6OHj2KPXv2DN0vTo5TqZRPL7OK07rj4LRjXRRdqxZJ8pafKOMxLUzkiRzVo9oGJbf9ww8/hG3bI9/kxxkn5HFzkv7R0acuqVQKjuNga2vLp89KpcIn0zpzoO9973sAgFdffXVsW/v888+H/n3lyhUA4DYWZ9vjmFMQUWLWJG3QfdbzPK3U6zuWcU9BU/YDx3FYvV5n1WqVlctlnk2gVCqxTqfDs1lAyoqgSg1IJ/spBZqYQouuUfYWOn2Pf52ap+whdMJdTCk26mQ8ySFnbKC0cCrEFFuUGUKuR5XdgbKsiBmAGLuTFYjaO0pOMZsFXVNlaYJGpgfKOkC6rVarPC0apV6julQyUvo8KiObzTLbtpXZQsQUpQT1Yblc5inZqM/Iphi7k0VgVGpFlYxR2qfSmWi7YgYDsU3Uj1HsnNojpoyltHFyX6raSDoT0z6KGWPENJKqzBaUuUhOZyhnU6KsKmR3lGlDTCvIWLg/UIo7ahvJR6kTwz4f1Re6fhZF56p6RkG6lv1ahGKbaAeifsW6dOKLiI7+xMwh5AtiXZSysFQqDaW2FFPuirZHaYJJV+vr60r5VBm2gqBsLKRLyjoTlJ0lLPNJnHFaTJcpZtrRHQcnjXVBMV1EV9eiLVKsFLPZUNt0x2PG/NlwKF2nLA+1I5/P+1KpihlxVPrWsXMZ3XFzkv7R1aeurdE14E46UYpXhM4cSGwX2We9XudyyOmERcR4KcdxMXPbNNoeNqcQyxvlD7oxS7cNqhiq+yyNEZOm4p4XYy8UGLsd2CnHLRkPGXSn0+HOLv6JQVT8I8SOJOemSbs4ARMnSHLgCapXhZxWj6A2BCHnYxYHH7lummTJ1+kZGiBt2x5ZJ+UZp+fz+bzv/qB2q2QK6kMKOKJTB8nY6XS4E1AZ8uRM1WZ6lnRI+e3J8UR7CrIVlX5kGXXap9JZkA0FtSmKndMEUexLVd+oIHsjncu2P6qvRcTfeAiyJZJV7F9agMmM8geqi3yVBkBxEhb2uY7OR/lZFJ2TLPLCVqVDnf5T2UFQm3T0GdSXQfqjfqS2iwM9xT+qh+K2bdusWq36UlGSTLZt80kHDZIqRqXpVCH+Boht2yMHVmrTKOKM0+LvFoh6CRsHGZss1unGB11dq/pc/I0WOV+97nhM6Sip71RjrriAonuD7FvWt46dq9AZNyfpHx19RrU1cXEZpMtRcyD5HlFf5Mf0skmFmB5V9HnZH6fV9qA5BWP6/sCYXszS9QdVDNV9lmxJ/p2i3UKCMc00QQaDIRZqtRo+++yzoWxaRLfbxdNPPx24hS+RSCCbzUbKAGGYTOeG8ej1esjn85HONhjGY7foOpfLodlsamcnNMyHSqWClZUVtFqtuWQIM+wsdsxhZoPhXoByLX/7299Wfn7//ffzVKeGeDA6nw+lUokfOjZMF6Nrg8EwLf5t3gIYDPcSX3zxBQaDAZ5++mm8+OKLQ3nKr1+/jkuXLuHtt99WPkuH1La3t+F53lR+GOpuZBKdG/TxPA8PPvggBoMBHMdBMpmcWkaze53dqGvxkHe3291xmegMd6CseNevXzffKBgw0RkFg8EQnVarxfL5/NBeWzo4GrZnFIo9nYZwxtG5ITq0h1dn77hhMnaTrqOcGzTMF7mfRp0BMNwbmDMKBoPBYDAYDAaDwYc5o2AwGAwGg8FgMBh8mIWCwWAwGAwGg8Fg8GEWCgaDwWAwGAwGg8GHWSgYDAaDwWAwGAwGH2ahYDAYDAaDwWAwGHyYhYLBYDAYDAaDwWDwYRYKBoPBYDAYDAaDwceuXih4nodarYZMJoNKpTJvcQz3MN1uF0tLS1hYWJh53RsbG0in00gkEkin06hUKshkMshkMjOXZRb+2G63kclkkEgksLCwgKWlJXieB8/zsLq6ioWFBSQSCWQyGTQajanJcTfQbrf4mgPDAAAgAElEQVRRKBSQy+UiPdfr9biu2+32lKTbXYyry3nT6/XmFrt2GmY+MZp7ec7VaDR2pX/Hwa5eKFy+fBkffPABtra2ZlJfIpFQ/hGVSkX5+b3mUPcavV4PH3/8MdbW1jAYDGZa9+rqKi5duoTt7W24rot0Oo0333xzpjIQtVpt6v7YaDRw+PBhnD9/Hv1+H8ViEWtra7h58yZyuRweeOAB3Lp1C61WC4PBAL/5zW+mJstup91u49y5c9jc3Iz0nOd5uHLlCjY3N2du7zuVcXU5b3q9Hs6ePYtarTZxX+ZyOeX4t1sWkrOIX7udWc+5dgrtdhvXrl3bdf4dG/P+aeioFIvFoX/TT8PP6ufgxZ+ir9frvs/7/T7L5/P8p8/7/f5M5LpbkPt3p5ZdrVZZq9UaupbNZtksXarf7zMArFqtzqzOMKbtj47jKPuxXq8zAMx13anUS0zTPucFxaqolMtlBsDnB/cy4+oyKq7rxupjccUu13WZZVkMACuVSjFINltmPZ8g4u7PaTIvHc0aVayflX/vNMb6RiHozQF93V+pVOB53qRrGB+1Wg29Xi/2cqOwb98+/v8HDx70fZ5MJvGtb30LAPDEE08gmUzOTLbdTq/Xw9ra2q4o+9ixY7GVNS4ff/wxAOD++++fsySzY2trC9/4xjd8169duwYASKVSU6t7J8QfgwEATp48OW8RlKRSKezZswcA8NRTT81Zmt3DTu3Pe5VpzkV2I2MtFM6fP49OpwMAKJfLYIyBMQbXdXHgwAGsrKzEvo+L9oAb7k48z0M+n98VZS8tLcF13djKM+x8TPwx7BRqtZqZxNxFmP7cWUxzLrJbGfuMwuLiou9aKpXCG2+8gWKxiK2trdj2JjYaDTz++OMYDAZoNptIJBLKhUi32+XfduRyOeXbv0ql4jv4OSu63S4KhQL/9kWuX7Wvs91uK89DALcDDLUlk8kgl8vx5+TDteKB19XVVQC3HYLkWVhYQK1WiySv53nY2Njg3yLp6F9Fr9fD3r17+b5Hua2e5/G2BB1SDdJFWNlRyeVyPKjv378/cA9upVLBwsICFhYWsLGx4fu80WgoD+TqQDaxf//+ITnIJ9rttu9wourczCjb0tG5qh3vv/++VhtkZFsrFArodrs++QFgZWVlqB2JRAIrKytD94l2qqNr8UAntZf6dVT8kX2oUCigUChotVk+GNjr9XhZ6XSat1+07Vwup7QTVVxT3SfrmWJB1P4YB/kMV7vdVp7rIsRvrqkvxHYGyTUqLuog2sso29fVpSy3qn+63S4/HE73ky2K9lqpVHDo0CEAd/wgqq3Lvh1kU3FCh9/T6TTa7TYajQbXxzgLcFV8Uo1fdI/nedyexL7UjV9h/iAedO12u0in01hYWAgdA8P6U1XvOPOqsHgujxlh45co36gxP8yXotrFqDit29ZR6M4XqA7yZ1WfTDLG7zgm2beEgH1qxWJxKntXodgfRvvl8vk8KxaLrNVqsfX1dX5NlqtYLLJ+v8/6/T6XM+peO/zrjEIQtHdXLteyLJbP53n9dJah0+nwe+iaqDtx3yfR6XSYZVn82Wq1yp9zXZfV6/WhvaK0p572oq6vr7Nisciq1erQveKZijB5W60Wv6aj/zBU+2T7/T5zHIetr69zXTiOM6SjUboYVfa4BO3NpjpEfZOsYh9Xq1XmOA7fT0/6irr3kWxflKPT6fD9+mJ7RXt3HIdfL5VKQ7LQvWE6p3aQLZE8ZEdRfKrT6TDbtnnZVI5lWb4zPkFlU5/I6Oia/Etsr23bvjMPqj4iu6fnstmsdj+2Wi3eJ6L/kF5t22bVapWVSqWhe+W939lsVtlGsZ8Zu+MnFAPFfhVl1u2PqGcURBsU20B2bNu2r78dx+FncKhdolxynNGJBaPodDpDNka2YFmW7z4dXTIW3j+u67JWq8VtrlQqsWKxyMrlMvcnsY1Be8R1bJ18m+4T+ySu+EhxUNR5vV7n7aO2iTZNvqdDv98f0r3KruQxkPxL1Jtu/Arzh06nM9SnFAN07S6oP+m6Kg6rzkeO0teoeE66IhsIG79051w6vhTFLnTitO7YFUbQfIH6uFgssnq9zm1Mjg9xjfE7hVgXCv1+XzlIkdLlYEQDjW4njlooyIOn4zhDnUeBWFVm1AAptmXUn6wb0chF2cVrYZNQ8T55IkATCvkZcfB1XVc5iZDr1ZV3lP6j6lXlnOVyOfAAO9lCFF3EQVgfifqmAEw6I73Kh26jDCyEaqEglydD/VKtVlmn01EeuNfROQVt1WQl6kIhm836DmQHLbajLBR0da2a3JdKJd8AqYo/8qDd7/cjDQZB/kMDpTwhkGUgfcttVL0EoUmMCMUD8bpuf4xzmJkO4Kte4sjtkHVJE2eRbDY7dE0nFoyC+kPsd2qneE1Xl1H6h+xS1L34kkh8QRM0voTZeqlUUi7AacIVB0GxjPQo+0vUCVSxWFSO5dQGVdwn3dF/o8QvXX+QF7uivYwiaKFg27YvDpONqfowCJ14ztjo8UuULcqcS8eXdO1CJ07rtjWMsIXCKBniHON3ChOnRxW3AXz1q1/Fj370I2SzWZw/f57f89vf/hbFYhEA+NkGAFheXoZt2+h0OkOHhMfhvvvuG/p3MpkcSvd27tw5uK4b+DX3OF/nsX+dzZD/yuWy795kMolbt27hyJEj8DwPlUoFhw8fBgB89tlnket++OGHsbW1NfQVdtDhMfFANR32DDtkHVVelf7j4MyZM1hbWxvqM9py02w2AUTTxSwQ2/7lL38ZwB2dXb58GYPBALZtD7WJ2vKnP/1pqrJtbm7CsiwsLS3h6aefxm9/+1tfX+no/OLFixgMBvjOd74z9GzUg9We56HZbOLQoUND9dFWoj/+8Y/jNlVL171eD81mE0888cTQs2+88QYYY8otliKO4+DVV1/l8SOZTPrK0kH2HzqwTfYTxC9/+UtYluU7xH3kyBEAwOnTpwHc3iKwtbXlk01+bpr9AdzWTz6fx+bm5tDX8JZlAQDOnj3Lr509e5bHHOD2eHHr1i0Ad36zoNlsDsX5SWPBvn37eL/TNgdKN/zFF18A0NcloN8/IuLWtVQqxcfOP//5z4Fy68aVtbU17Nmzx+fz6XQ6sOy4EW066jjheR7W1tbgOI7vs1deeQUAlNuHyI/pv7rxK6o/pNNp3qaw2DGKRqMB13V9ZaRSKeTzeQwGA1y8eFGrLJ14LqIav1SEzbl0fElklF3oxumobR0HWTbSA7Vp3mP8NJh4oSAfZi6Xy2g2m9i7dy/fw5dMJvH666/Dsqyh/W61Wg0HDhyYyKF0uXr1KrLZbODkftKFii6VSgV79+4FABw/fnzscg4ePIhyuYyVlRU8+OCDqFQqeOihh2JvR1zyjsvW1taQjcl/wOx0EQeUnSeoPcvLy1OtP5VK4f/+7/8wGAyGBjURHZ3//ve/B4CJdUyZm1qtlrIu8YVDVHR0/Y9//GMi+d955x0At8+J0F74afehyNbWFmzb9l2nmEqH7mmQkidGMtPsD+L5558HcGdR4HkeBoMBstns0MT5nXfe8Z33oD3Rx48fx/PPP49sNjv0eRyxgPYfHzhwAI899hifqBO6ugT0+2cU//mf/wlg9AslHVtvt9sYDAZjLWR3CmSfKp1+85vfBACts3G68WsW/qCC+vPrX/+67zPKqvjpp59qlaUTz6dFmC/pohun59lWYt5j/DSI9QfXUqkUlpeXsb6+Dtd18b//+7/8s2QyiZ/+9KdYW1vjjvzee+/hueeei1OEkWxvb8+sLhWZTAZnzpzB5cuXsby8PHFay+XlZbiui0KhwAfGSQ8cisQt77h88sknofdMWxdxM880m59//jls20az2Qw8zK+jcyC+dty8eTOWclRMU9eLi4v46KOPUK1Wsb29jf379888O1KUHz/S1fM0++PgwYOwbRu/+MUvANxeMDz33HM4fPgwXNdFrVZDu93GgQMHhp5rNBp45JFH+A/uqdJTA5PFgl6vhwcffBDA7fGiUCj43pwSujqa9Mepwr5VEtGxdV3f3slcvXo1lnJ0Y8M0/WEUNOmclHn0eRRfipOdYt93UyrtqfwyM32tKv+K3Q9/+ENYloWTJ0+i1+the3t7Zm99H330UbiuG3g6fdqdWqvVsLW1hddeey3W31ZIpVI4deoU39L1wgsvxFLutOSNiuM4qNVqymwBcjaGaekiTh5++GEAw1ssCMqCM03a7TY+/PBDXL58GY7jYGVlxTeJ0tH5V77yFQDA3/72t4nkeeihhwDcfmmgYlTGjTB0dP21r30NwO2vrFXo9kehUMD29jb/pWjdLBuTQm9WgybC9MadBui//vWvI8ubZn+IvPjiizweX7p0Cfv27UOhUIBlWXjvvffw/vvv44c//OHQMy+99BIcx9HKKjVuLDh58iQGgwFef/31wHt0dQno988oaJJK9qxCx9a/9KUvAQA++uij0Dp3KuSvV65cCbxH5xsT3fg1K3+QeeCBBwAAf/nLXwLv0flGC4g2hsaJji/pohun59VWkXmP8dNgKgsFmozL+wjFbxVKpRLfUzgLaJ/qd7/73aHFQq/Xw1tvvTXVH2oC1F8Tfv75575r5PziGwzP83zfhlA6RWJxcRHFYjG2/P668k6bZ599FoPBwJd6rd1u88F32rqIk71798KyLKysrAwFLs/zcPToUf5jRTqQjej2S6/Xw/Hjx/H6668jmUzy8wpPP/30UGDV0fn3vvc9AMCrr746Ucq3ZDKJbDaLZrPpSx9XqVSG3kCRLKotGPQWSXxeR9epVAqO42Bra8s3kNC2lVHI3x7QoPj3v/995HNx8eKLLwLwD4KkK9rj/+STTwK4vUd91Nv1KP0xCc888wyXj2wJAD930Ov1fDFZ5c+y7U0aC1QvjGR709UloN8/InKbPvzwQ9i2HfgNCqBn64uLi7BtG1tbW1qTlV6vt+O+lU2lUshmsxgMBr42UDwk2xqFbvyalT/IPPnkk7AsC5ubmz6b/OSTT2DbtvZLVp14Pg10fEkX3Tg9r7aKxDnG7xgiH3/+F5T6Sjxh3u/3WbVa5VkaVKe76UR4lFP7BP6VVYDSZzF258S5mEWj3+/zDAji6XoxDRxlrQiSM6zdgDqlG2XqwL9O2VMbKXuA4zisXq+zcrnM7ysWi0Np3cT7qtXq0L2lUol1Oh2e3YNkp1RhVE6QDkh+x3GG9C+nl9OVlzIWyHagqjsMKr9er7NSqcTT91GmHtIpZVcg+cN0EVQ2Zb6wbTuSnGLGi2q1yqrVqjI7CWNMmTaOMmuQPVPmlqhpesXUtCLkY6IslCZOzqIjpkwlferonLE7GZSo3Hq9zvUsprUMg1JN4l/ZPLLZLNeLCOlSTqMp2pvskzq6Fn2aUvRls1lf9gxV/CE7EG1RlfEiiKBUwtS3cgYc0pFYPvWDnBJQlp9ipWVZPP0h9T9dc11Xuz/EVMvjkM/nfdlrqC9UtkPtLJfLrF6v8+fJrylWhsWCUYjjCelH1C/Zja4uRbnD+of0mc/nfWkVRb8V01NSbGZMz9bFNJiUilJMw7q+vs51p4pnYYhxUI5nqqxO1N+q1LhhdViW5UtZKvuLKhMSoRu/dPxBTPGr6/vys3J/imOwaA9i+l8ddOK5OGaI8qvik+6cS9eXdO1CJ07rjl1hqOYL1B+yrVKsFuNgXGP8TmGshYKc7lT8o9z7oybflCc6KmJ+cUrtKNdPTidfI8rlMjdocUDRIajNYtmqz+XBhfKjB01UabFlWRYf4MghxDRgYvtFI1TpoFwuK+VT3UsBMExeXf3rOgcFZDngU55sMSe2GMxG6WJU2WIwimKPYjCi1IuqNsv6EQeWer3Oy7BtO9JkS1WfOPCH9QnZvMoedHUu3iMGb8qLLU6edeh0Ojzgkt2Lz6vam81mA31OzuEeputOpzN0j8pm5fjD2O0UhhRPSCbdQVzlP6q2BrVTTGVMaS9Hyc8YG5oUkqyUk17s36j9MU5+cHFSJCKnHxRlIl3T77tQjBJfXITFglGIL3ocxxn6XRI5lbCuLnX7R3wZRPcGjVHkd7JMOrYu5q63bZtPkOVxmyZYuhPfoHlB0BgzyqbDcF2X2yfpSbWYGjUORYlfo/xhknYQQf0p/pYB2X2URYLcVlU81x2/SD6d/m21Wlq+FNUudOK0ztgVhjxf0J07yYvyccf4nUaCsRkdBRfIZDJ45513ZpLtyGAIo91u4/3338epU6fmLYrBYLhHyeVyaDabM8vOYjAYDDpM5YzCKGhfoVkkGHYK77//Pl5++eV5i2EwGAwGg8Gwo/i3WVTS7XbxyCOP8H+3Wq1ZVGswjKTdbuPcuXN4+eWXp36Y3WAwGIIQE1Z0u13zIs1gMOwYZrL1qNfrwXEcLCws4Pjx41rp7QwGg8FguNtpt9v812OJcrm8K3+YyWAw3H3M5YyCwWAwGAwGg8Fg2NnM/IyCwWAwGAwGg8Fg2PmYhYLBYDAYDAaDwWDwYRYKBoPBYDAYDAaDwYdZKBgMBoPBYDAYDAYfZqFgMBgMBoPBYDAYfJiFgsFgMBgMBoPBYPBhFgoGg8FgMBgMBoPBh1kojEGj0UAikUCj0Zi3KLuOXq+HSqWCdDqNdrsden8mk0Emk5mBZNPF87xI7Z4G89Rlr9fD6uoqFhYW5tb+nUitVkMul0Mul5u3KDuWRqOBQqGwa3UU1ffJT3q93gykmy61Wg2ZTAaVSiXSc+12G0tLS0gkEmPV63ke12MikUAmk7nrxuuweUi73UYmk0EikcDCwgKWlpawtLQ0F9u6m2x6HMb1g52CWSgYZsqVK1dw+vRpuK47b1FmyrvvvoszZ87cc+0Gbg/aV65cwebmJgaDwbzF2TE0Gg18+umnaDab8xZlx9Jut3Ht2jVsbm7OpL5EIqH9pzvoX7x48Z70/Vqthg8++ABbW1uRnut2u7h58ybW1tbGrjuXy+GBBx7ArVu30Gq1MBgM8Jvf/Gbs8nYbjUYDhw8fxvnz59Hv91EsFrG2tnbPTtTnybh+sKNgBsMUqVarrNVqDV0rl8sMgO/6TsJ1XVYul2Mtcze0e5oEtX8aut5NAGDZbHbeYuwYisWi79qsdGTbNut0OvzfrVbLV3e/32fFYjGSzcbl+yrd7GRIf+P4dzabZeNMUer1OgPAXNeN/OzdguM4c7OV3Wajs2ASP9gJmG8UDFPl2LFj8xZhLE6ePDlvEe4ZjK4NRK/Xm+hN8qQcP34ci4uLI+9JJpM4deoU7rvvvhlJdZtarWbeCGtw7do1AEAqlZqzJPNja2sL3/jGN2Ze77z91zAdzELBMDWWlpZ25dfttVrNBLsZYXRtIDzPQz6fn6sMhUJB+94jR45MUZJhut0ulpaWZlafwRCVneC/hukw1kKh1+thY2ODH85qNBpIp9NIJBLI5XLodru+Z+gwVyKRQDqdHtrfKR9wXV1dRSKRwOrqKoDbkwl6NpPJIJfLod1uo91uD+0ZFeuia/IBOFGORCKBQqHgk1c8BJXL5YYOgHqeh42NjaGDKWJ9cp3iZ2KbG42G76CR53nKOrrdLnK5HC9bfqvkeR4/pBR0cCtIh0S32+XypNNpFAqFiQ7e5HI5PgHcv38/EomE8iCfaDvyQEgH2hYWFoauTyrrqOcrlQoOHToEAFhZWeE6j2pn9Llo83/5y1+Uz8rlBtn1uG2iMmVdyn4nHn7L5XLcHkV/IJ8EhvdzU99GkV2la5JZtmnZR4DbNk2HXOl+ar/K1kbFIFGPhUJhKD6IZYm2ID6v0oVOv6hkjMMuxomNo+IeEE+cCaLX62Hv3r18H29QG3X6eVRsnQaq+ka9+Rd1VCgUhmSjQ/9i2xqNBh5//HEMBgM0m83AeCMzqX8Tqn6v1Wpaunj//fe1dTZpH5Ftr6ysALhjQ6K/hfm353lDB09rtRoWFha0kkDIfiuWq/LFMH+ixeHCwgI8z+Oxp9FoKOchYj3AnZhK/1bZFtHr9XyyyPeMmjuN8t9R9erYljx2VSoVLCwsYGFhARsbG6H9EsY0xhpdP9g1jLNfqV6vs3w+zwCwUqnEstksK5fLzHEcBoDZts36/T6/v1gssmKxyPr9Pt/fCWG/Vr1e589ms1lWLBaZZVksm82yTqfDLMvi+0ar1erQXs9+v89s2/btZaR9iuLe0vX19aFnad9YPp8fusdxHC5rqVQaKltsu7jfjPagOo7j01epVBrat1etVpnjOHwPJclFsrZaLV5HPp9nxWKRtVotfp8ob7/fZ47jsPX1dcbY7f3epEtqZ5gOGWPMsixWrVZ5/bZtT7yfLmhfLl2nfb6tVovbBLWj0+nwPpT7dlJZw55X7SeMYmeMMW7D1HbqO1EfdM2yrCF/oXJlPxq3TUG6FP1O7Auy+WKxyEqlEltfX2etVovfW6/XeRlkp2Ifu67LLMvy6UplD0F7N/P5PLNtm/sIPSvah+hzxWKR1et1Vq/XmWVZzLIsX3+MikGiLCpfEttM/SbbB+2pjupXQXFqUruIGhtHxb244kwYQfvSdfs5LLZGRXVGQaRcLg/5eavV4nKJe+TFmEe+Rn0j6p36V6W3qO2Iw7/7/T6zLEvpO6VSaag+6m+yEdKF7Cu6fTTuGQXStYyOf4tjUTab5XFIRw7Xdfm9og4Zu6NH8o0wf3Jdl9u4OA8gXQbNQwhVfAqyLYrXoix0n9xHo+ZOjPn7bFS9OrZFeqCxq1Qq8XOPpC/xTNE4xD3W6PrBbmLsw8xi4BOhzhOVZNu2v2Jp0iJ3juu6rN/v8wWISKlUGjK4UYOLGHwoqItks9mha9ls1hcA5fqDJje2bSsH92w2y6+Rc8gHreRJBtWhkkXWm9wH8uAWpkO6X5S7Xq9PfaEgD0g6gWdSWXWeD+pfXTujwCa3m4KgeJ2CvWwP5XKZ+0IcbQqSX9UXjDEe2MUyVXoJ6uNRdeksFGiQFlHZh2pxThMh0b51YpBt2z5fcl3XN2kPkllun26/qCZJcdgFY/o2Gxb34ogzk8ob1M/i5EsntkZh1EKBbIMWggRNFOSxRxzfZNnE60E+Nc6CZ1L/LhaLSt+RJ3402ZRtm3RB16P0UdwLhaj+TbGm3+9rH4yW20vQAk2UMcyfGLujA7Jx1UF7nYWCWK9Kz7Jdyb6lM3cS5dWpV9e2xHLlWBrH5DvOsUbXD3YbE59ReO6554b+/dprrwEALl26BAA4d+4cXNcd+XW6yDe/+U0Atw8iJZNJPPzww9ja2kKlUuFfBT311FNjybq8vIxbt27xeguFAprN5lDKxkcffRRra2tDX389++yzWuUfP34cg8EAFy9e5Ne63S5vCwBcvnwZg8EAtm0P6YNSJP7pT38aKlM+MEflEGfOnMHa2tpQWfv37wcAXmaYDh966CEAwNGjR/lX5gcPHtRq8yR8+ctf5v9P7frnP/858plJZZ1FWykN3759+4auqw6XnThxAsDwgV7P83D69Gk888wzWvXF0SaxLwAgnU4D8NvbrDh//jz3QdoKAKjtQ5ZR9hmdGNRoNOC6ru8gayqVQj6f9/m1DpP0Sxx2EYWwuBdHnJmUoH7+4osvAESPrZPy7rvvArjTz0ShUIBlWWg2m74tSDS+EeVyGcCd8XJajOPfnudhbW0NjuP4PnvllVcAgG+puHjxIgaDAb7zne8M3Xf//fcP/XvWfUSM49/f+ta3ANzWke7B6EKhANu2cfr06aHrv/71r/HDH/6Q/1vHn0RI7rCD9lHp9XpoNpt44oknhq6/8cYbYIzx+nTmTlGIYlsior3KNj0ucY41un6w24j9MPPevXsB3FHy1atXkc1mwW5/e+H7kydTMgcPHkS5XMbKygoefPBBVCoVPPTQQ6HPBUH7/Y8fP47nn38e2Wx26PMf//jHOHDgAA4dOoR0Oo1arYbl5WWtsp988klYljWU6WdjY2Po0BtlZAjSh25dxNbWFsrlcmB5QLgOk8kk6vU6Lly4ANu2+R7bqLLMgkllnUVbNzc3fXYVRCqV4jmuab/nxYsXceDAAe1J+m7qvyjQ+ZUPP/wQ77zzztjl6MQg8suvf/3rvudpwvDpp59GqneSfonDLqIQFvfiiDPTJu7YGsbVq1cBqCcBe/bsAQD84x//GFkGTcTCXpDMg48//hgAYNu27zNa8NBC6Pe//z0A/8sRmVn3kVxvnP4dxIsvvgjXdfnkk3QkLjZ0/GkWhNmnSNjcKQpRbGsWxDXW6PrBbiP2hYJqENve3p6ozOXlZbiui0KhwAch1YHpMBqNBh555BH+QySqt3vJZBK1Wg2tVgvpdBqHDh1CJpPROmiVTCZRLBbhui4/cNTr9ZRvAeJ0gk8++ST0njAdHjx4EDdu3EC5XEatVoNt24EH1ubNpLLOoq1RbP7ll18GAH4I79ixY/yaLrup/3RYXV3FD37wA/zud7/DqVOnJn6TptsfNKGIi0n6JQ670EUn7sURZ2bBrFOI0qTnboUWRDro6n5eaV7j9m8V9M3BL3/5SwC3v3n67//+b999Ov60U9CZO41DFNuaFnGPNcD87HtaxL5QIAU9+uij/L+u6waeDNdVaCqVwqlTp9DpdAAAL7zwQmTZXnrpJTiOo5UCb9++fTh//jyq1Sq2trbwq1/9SqsOChJvvfUW3n33XRw+fHjo84cffhgAcPbsWd+zlHEhCo7joFarKRcyckaAMB0mk0ksLy/jxo0bcBwHhw4dmmq2kEmYVNZpttWyLLiuq10evT1uNps8O8Q4OcB3U/+Notfr4cSJEygWi7EEbZ0Y9MADDwDAUGYqGfnrZF3G7Ze47CIKQXEvzjgzLeKOrWFQX1y/fj3wnrA3izT+ff/7349PsJj42te+BgC4cuVK4D20ZeUrX/kKAOBvf/vbyDJn3UfENP1bhl4Ybm1todFo4MKFCz47iOJP04T6+MyZM8rPqT+izJ2i1KtjW8lZIMMAACAASURBVNMk7rFG1w92GxMvFD7//POhf1PH04SZ9qh+97vfHRqoe70e3nrrrdCBr1KpDC0mFhcX+Vt7ggxKvE/1FkuV0192VDlFZ6FQgOM42r8HIA7uZ86c8TnW3r17YVkWVlZWhgKC53k4evQo/8pal2effRaDwcCXNrXdbnMdhOmw3W4PBehkMsnPmty8eTOSPNNmUlkneV7XzqjPf/7zn4fKQ9Cb4hMnTuCll17Sfg6Yb//R4CrW43neRN8iqr4On2TBoxODaNvg5uam7+XFJ598Atu2+WBP/5XfCMqTkDj6ZRK7APRtNizuxRFnpk3csTUMOp/3i1/8wvfZ9vY2isViaBlnz56FZVl48sknY5UtDlKpFLLZLAaDgW8CT/ZL52W+973vAQBeffXVkb46iz4ivxTliOLfcUB++4Mf/AA/+clPfJ/r+NO4UHmfffZZ6L2pVAqO42Bra8u3QKGtg4De3CkKUWxrmsQ91uj6wa5j3FPQYrooOTWcnD2Dsr3Q/XKGg36/H5jqizJpiKfKbdseqoNOlOfzedZqtXj6MPwrm0G5XObpyKgOSi8mpjOrVqv8BDxlOaDMAmLWCGq7fCqeCPu5bpIXuJ1KlrIHqLLJiJkRxHSHcpo1Ki+bzTLHcYayOITpkPqNsndQf4gn/KlMOcPHKKgN5XKZVatVVq1Wh9JIipk+Op0O14ecIUpsr46so9B5Xsx6IWap0bUzMT0o3VutVnnfUXo1maAsEHG0SaVLxu5k1xH7QrQzMduHmM2F+oiyhjiOw32oXC7zPi6VSrw+Vb+rdE0ZsKhN1WqVy0kpBTudDn+W0kuOalNYDGLsThYNOXWjmNaQoAxn1WqV1et1ViqVeAYMaksUWxuV8nRcuxD7LMxmw+JeHHFGB+oX0qmYHlHWkaqfdWJrFKiOoP4RU42KKR5lmxRTgJIt0TU5pqr8hDHG5aAUnlHkn8S/KQWkPM7LcpN9UCyo1+u8LY7j8Pt1+igoXoUhtk3Wn65/i+O7bnpqFdQuVRk6/iSmKVVl7Aqah5BdqWx21NhLdkzxW7QxnbmTWL7ov0H16tqWaAuivarSxUedp0xjrNH1g91ELOlRyWBs2w5M3SfnjRYNn4xUdBrxOVKyOLjJUMC2bZt3BN1LxtXpdLgMFAToORrMaCAXA5k4qRNlkWUVkQ1KRsxxLetNroMChXxNTDlHefupbXIO71E6pJzE9Lw48BGWZTHbtiNNWMRgSPpV9TXZUlh7W62Wlqyj0H1ezKUtfqZjZ4zdtjXSOX1OE6mgQBE19aVum4J0qeqLIDtT2SRRrVZ5wKd+poGGBuBRPq7StapMcRDQtRk5lgTFIFmX9Hw+n1dOVFSxhPQk5mQf1S9BbZAZ1y4IHZsNi3uMTR5ndKDfYojaz2I9o2KrLqo6guL9+vo6twUxL7wM/X7AKNsa1S6aYNu2rTV5jsu/XdflEyLyHdWLDjEPPtk65dCnBSkxqo/C+jcIla3IPhXm36P0EBWaLAcxyp/CdBA0Dxlls6PK63Q6Q/0h61tn7kT3if4bVm+YbUW113HmKXGPNVH8YLeQYGy8I/aVSgUrKytotVp33QlvQzDpdHriw+kGNel0GpcvX55bSlLDzsTYhcFgMOhj5inxEvthZsPdy8bGBt566615i3FXsrGxMbXUl4bdi7ELg8Fg0MfMU+Ln38Z9kA7uXb9+3XyjcJfT6/Xw7rvv4qmnnjJ9HSO5XA7NZpMfGr1x48a8RTLsAIxdGAwGQzTMPGV6jPWNQiKRwObmJgDgRz/6EXK5XKxCGXYWqVQKb7zxhnG+mKEUwslkEpcuXTJvjQ0AjF0YDAZDVMw8ZXqMfUbBYDAYDAaDwWAw3L2YMwoGg8FgMBgMBoPBh1koGAwGg8FgMBgMBh9moWAwGAwGg8FgMBh8mIWCwWAwGAwGg8Fg8GEWCgaDwWAwGAwGg8GHWSgYDAaDwWAwGAwGH2ahYDAYDAaDwWAwGHzEslDY2NhAOp1GIpFAOp1GpVKJo9jY8DwPlUoF6XQa7XY78vPdbhdLS0tYWFiYgnTR8TwPtVoNmUxmx+l6Gniex23sXmivikwmg0wmM5Nya7UacrncXfVDirvNZ1ZXV7GwsIBerzfW841GA4lEAo1GI2bJ7tBut7G0tIREIjG1OuJi3L7v9Xq8L8YZO6LS6/UmGqumSa/XQ6FQQCKRQCKRQKFQQLfbnbdYWjQaDRQKBa2Y1uv1sLCwgNXV1RlIFj+9Xi/W+cpui52G+Jl4obC6uopLly5he3sbrusinU7jzTffjEO22Hj33Xdx5swZuK4b+dler4ePP/4Ya2trGAwGU5AuOpcvX8YHH3yAra2teYsyEy5evIhLly6N1X+GaDQaDXz66adoNpvzFiVW7jWfmTbdbhc3b97E2travEUJpVarjdX3nufhypUr2NzcHCv2t9ttPqnW+Wu327hy5QpOnz6942Jdr9eD4zj42c9+BsYY1tfXceHCBfz5z3+et2ihtNttXLt2DZubm/MWZer0ej2cPXsWtVottvmKiZ0GsAno9/sMAKtWq5MUMxPK5TIDwFqtVui91WrVd182m2UTqstHsVgc+95Wq8UAsHK5HKtMO5VR7Y2iR4MeAFg2m51JXa7rTsWO73WfmQXTiIvTYJK+jzJ2yHXm83nmuq6vLFEO13WZ4zi8/HHrmyalUonZtj1vMbRRjQmzjGlRUM03JiVuv9wtsXNaY8m9zkTfKHz88ccAgPvvv3+SYnYcx44dm3odtVpNe1tBlHvvNYxudj8nT56MvUxjF4adwIkTJ5BKpUbek0ql8M4778xIovG4evUq0un0vMXQotfr7YpvuohZzDfuFaYxlhjMYWYfS0tLU//al848xH3vvYbRze6nVqvFPqgbuzDsBPbt2xe6SCAWFxexb9++KUt09+N5HvL5/LzF0GYW8417hWmMJYbbjLVQoL2X+/fvBwDs378fiURi6KCQ53n8QE0ikUAmk0GtVuOf53I5vjdTPCAj79kE7hwoowNejUaDH54OmhDQgTA6YP2Xv/wltF25XI4bGrVJdaCsUqlgYWEBCwsL2NjYGPpMbvfCwgKWlpbgeR6A23vAH3/8cQwGAzSbTZ/eRHTv7Xa7XJ+5XM73JlXVF2GHHOVDdaurq0gkEkMHvBqNBjKZjLKdVC8dfltYWEChUEChUACg7md5T+8oRulmVL2jEG1GdVhPPNTueR7XuahL2e50Dn+FyUuHRsXDaXL/tNtt3he5XI73Ax3ElPsuqNxRiP2tsiMdmxGpVCo4dOgQAGBlZcUXC7rdru/wZNgBz7h8huQbJ0lDWOwjOelwZbfbRTqd5oeX5XgnIh8olf+o/o2NDd/hw1qtxuskGaltcj2y7idJJCAfhhTbkE6nuY/VajWub9GGo+hV1K8Ym95//33lfSob20kHdMPGujD5R9kZED42kI80m03uT/K4qBoHRH+KYyzRodfrYe/evXwvfdA4Emb7op/I16kvMpkMcrlcaDzqdru8Xel0GoVCgfuRar4hzovE+oPmS2J7qA9l36lUKso4AUQbc8U2jYqdYeNEmF6CGDVOBo0lOranM85EiZ3AcIIf+W9XHgifZN8S7VuT99f1+31mWRYrFous3++zfr/PisUiA8BKpRK/b3193bfvrd/v8/11VG69Xme2bTMArFgssnK5zFqtFi9zfX19qP5iscgsy+LPUz0qWWWC9oeSTKVSie8pdByHAWCdToffl8/nmW3bfF8qlSfLiAj7JVX3ku7z+TwrFous1WrxdubzeX5fv99njuPw+mk/bJgu6vU6vy+bzXKdkhzVapU5jsPbSXWLcpJsVG82m/V9Lsvhui6zLMu3vzJoj6RKN2H1qiD5SRZRv1ROvV7nslEdokzFYlFp82F7JkfJ2+l0WL1e5/ZLiP0j+kSpVOLXSqUSW19fH7LVer0+stwgvXY6naG2uK7LbNtmlmUpZVLZjIqgfqXrKrulNoxiEp9hbPy+1Il9nU6H1+s4Du9vAOwPf/jDULwTfYPKzufzvGzSiRhf6vU69y2St9Pp8FjkOA4rFousXq9zmxb7kTHmq4fKE2MdY3p7ocVYLeq+Wq0yAMy2bVatVlmpVBq6VxwrdMcUxhgvl3TSarW434r91+l0mG3bQz5Puuj3+/y+OM8MqM4oBN0TNtaFyT/KzlqtVqSxISiGlsvlobFWlIHGhjjGkigE2aSO7bdaLa5/Of5ZlsXtn2wszCYsy+JnOFutFrNte6jvVbZFsVZuP8V2eb7kOA7Xn+gXog5Ip7JtM3ZnfiVfF9GNnTrjhI5eVISN66qxJMz2dMaZqLFT7lOyFfm+3cRUFgrFYlF58Eke/IImCSrnoWviJIEOU4vGQpMflUxxLBREZ6K6xOCdzWZ9kw7VRCSuhYI8SDqOM2SQ5XI58FBnWP3yIocCEQ3a4iE9xtjQIERyy/0l1hmma5XMOguFsHqD2io7cjabVV4TJ0z0Xwp2MkET8ajyqnSi8gnG7viZaKtB+hs1qKqCsDhRpPpV12SbCSJILtu2fXbrum7gYBcmv1hXmM9M0pe6sY/KEwdolR7F++XFLD0zqq0qf3EcZ+gaTUCoXPJvMa7JAyqhe2gySPcUl2Ubltukq1d60SC3mwZr8Xo2m/Ul4lBN5Oe1UFCNdeI4oit/kJ1FGRtUCwXyR1kG0rUq1o87lkQhbKEgQrYvL4BV8queHSUf6VKeM+jYlq5Pl0olZTwkvxChxb6s63K57PProLboxM6wcUJHLyrCxsmw+aRqTIoyzujETsaY8uWY6qXobiL2Mwqe52FtbQ2O4/g+e+WVVwAg8GtgHb785S/z/08mk77Pf/Ob3wCAb7/nN77xjbHrFBHrJFk+++wzfu38+fP863D6uh0A/vnPf8ZSv8x9993nk09Mi3bmzBmsra0NffVFW8Z0U2B+85vfBHD70F0ymcTly5cxGAxg2/ZQuVTen/70JwCA4zh49dVX+VdzyWQSTzzxxGQN1mCcepeXl3Hr1i0At7+OLRQKaDabgSnmFhcXh/577tw5uK478iveOOUVEX0CAD90qPKPcdm3bx8YY1hcXOR5uikN8hdffOG7X7aZKDQaDbiuy3VLpFIp5PN5DAYDXLx4ccyWhPvMuH05TuxLp9NcP3J7ZcQ4Q4Q9o0LuD5U+bt26hSNHjvDfoDl8+HCgDFGQ66K4LNuwSBS9Xrx4EYPBAN/5zneG7pMTbnieh2aziUOHDg318crKCgDgj3/8Y8SWxY9qrKNxJKr8KjubdGx49913AQAPPfTQ0PVCoQDLstBsNn3bUsYdS+IiyPZVMUzk4YcfxtbWFiqVCt/W89RTT418hvRy9OhRroeDBw+OJXcQa2tr2LNnj69dqoPnJ06cADB84NfzPJw+fRrPPPOMVn1hsVNnnBhXL5OOk7LtjTPOhMVOAMo5w7e+9S1tOXcisS8UKBOSbdu+z6ijppmNZHNzE9lsdmrl60D7Sj/88MO5Z7PY2tpCuVwGu/3tke9vHK5duwYAgWUuLy8DAG/7/v37+X5O+myajFsv7b08fvw4nn/++Uh2dPXqVWSz2UCdjDqoOC89RYX2Zh44cACPPfYYisXiVOoh+/r617/u+4wC7qeffjqVuoHx+3Lase+BBx4AANy8eZNfo0mL7qHZKFQqFezduxcAcPz48djL1yWKXn//+98D8L8oCiqz1Wop+/j8+fOxyT8N4pB/0rHh6tWrANRZD/fs2QMA+Mc//jGyDN2xZN4cPHgQ5XIZKysrePDBB1GpVPDQQw+NtLNkMol6vY4LFy7Atm1+diOuNrXbbQwGA+3JciqVQrFYxNraGj/HcvHiRRw4cCDWl0ph48S4eol7nJzWOGPbNra3t4eu0QuWr33ta5HL2wlMLesRBZF5IHfSLFldXcUPfvAD/O53v8OpU6fGeuMXN5988slUyg2b9CwuLuKjjz5CtVrF9vY29u/fP5NsNOPU22g08Mgjj+Dw4cM4f/78WG9+xrW7eekpCr1eDw8++CCA2+0sFArKtylxQoF8HkwSQ6YV+wqFArLZLI4dO8Z97+c//zksy8LLL78ca12ZTAZnzpzB5cuXsby8vCNSYEfRq+6CTFx07UYmlT+OsYEWLZOwG1IZLy8vw3VdFAoFvmAIO/h+8OBB3LhxA+VyGbVaDbZtBx7AH5cofUhxgr55OnbsWKyxQ3ecGEcv0xon4x5n3nrrLdy6dYsfWu52u1hbW0OpVJrKC51ZEPtCgVZMV65cCbxnmttPLMuC67qRMybEQa/Xw4kTJ1AsFnfEAgG4/XVdrVZT6kPO2KTLww8/DAA4e/as7zPKcCJSKBSwvb3N32aEZVyKiyj1vvTSS3AcRys7kopHH30Urusqt6U0Gg2tgXBeetLh5MmTGAwGeP3116deF705H5WpTN5aEifj9uUsYl+5XIZlWXAcB4lEAr1eD5cuXYp1AKrVatja2sJrr70W65vGcYmi16985SsAgL/97W8jy6TtD++9957y83Fj46yIQ/5JxwayuevXrwfeE/bNTtSxZN6kUimcOnUKnU4HAPDCCy+EPpNMJrG8vIwbN27AcRwcOnQolvnJl770JQDARx99pP0MfavQbDZ5ZrU4Y0eUcWJcvcQ1Tk5rnDl48CAKhQLOnDmDRCKBxx9/HD/96U/xxhtvjCXnTmCihQK9zfj888/5tVQqhWw2i8Fg4HNyup/2w1EQkVfEOqlMg6CJ3s9//vOxyxgX1des81iwiDz77LMYDAa+NGbtdnvsNIB79+6FZVlYWVkZGlA8z8PRo0f5187yap+Cx9///ncAd5xQ3kox6TdCYfWqUOWyjtJ3tF/1u9/97tAEs9fr4a233hoZjMeRd9aoJseT7lcP4sknn4RlWdjc3PTV+8knn8C27anmnB+3L6PEvnHo9Xp4/PHHcf78edy6dYtvMYn7pYTq63Yxxs+aKHr93ve+BwB49dVXR/pvMplENptFs9n0peKsVCpa35a12+25xfc45I8yNmxvb/va+txzzwEAfvGLX/jKpolcGLpjybyhVJvE4uIiisXiyN9AaLfbQ/aaTCbx2muvAQj/JiibzfrGQXlBtri4CNu2sbW1FWlBRd8gnDhxAi+99JL2czrojBPj6iXucXJa48zq6iosy8JHH30Exhhu3bq1Y7bQjc0kJ6HpJLec0YcyT6jSpskZEih1VrVaZfV6nZVKJX6SPJ/P81PulMlAfJ4yfogZHcT0mvl8nqfhE9OrjkqvKGaNqFarrFqtDpUpnuaX04NRZgpqT7Va5TqiNHT0PMlNqe9GobpX1BHR7/d5O6keMYUi/pVJgTIVjMocI6ZEVGUjoMwWJBtlCJKzbZTLZV4PZRairAuUXcBxHFav11m1WmXlcnkoFa2YoUNlayrdhNWrgnRULpd5eknSJclGqd4QkL1ATEtH6QiD7pXbMEpeygwi2x/1j5ixQrQBsb1iJhKqJ6hcyh4h+pVob6RrMTUnyT/KZlSIaffEzBeUUUxOmyimKAzT6bg+w9j4fakb+0Qdq+yS6hP7VkzRR1loKIVmUPYbsa1Up+M4Q74v25Goe+oTkofS7pLuVPajIigNLdUtyi9mHRFjhe6YQnZJ8tfrdS6/4zj8fkp5SXVls1key8L6Iqg9oxBtLeg5MT24nApV9kkd+UfZme7YQHWr+llMxyymrRVtLI6xhOqRs+6oIP3RfIJSW8v6Y0wdQ1Xxj7Ieidm1bNseKY9sn6QHMXOOar7B2J34I6a9FlPdyr4q3kvjB7UraKxSZRELQjd26owTOnpRETZOqsaSMNvTHWd0YyfJSf5If+VymZXL5ZFzkJ3MWAsFUpr8J3aE67pciWLQlqFc0NTB/X6fD0x0v1wPKV6+Tg7R6XR4sKCAQ44uDyoyYvCk9GeqdlL5okyM3Q56NKBREKHgK9Yt5hAPG2Tle+W6qe1B+qDgLS6gwgxWpXMZMUexbdu+FGu2bfO+pTLktqr0RRMgulfV3lF61KlXRmWH4uAUZAcy5XKZlyMOLKMYJW9Qv6r6R9dWR9lLkF+JExjHcYZ+h4EWHzo2o4IGRXERQ20XJzL5fF5rkcDY5D4zbl8yFh77RsUuxvy+R3ZGA7OoE5W+VbEpqM6gMsj26TcOaKJO+tT1B5U85L+qukfpRndMkXPJ08shytUuT4KpTIpBckpElX7I9nUmrkF9rtKZrk505A+zM1FXQWODqu/E+hm7PbEiPxF/6yJMhyJhY0k+n/f9PkMQtICiMVfX9mmeoGqrHEdVKXhlKH6RbskWRd3I8w3xuhxvKW1ztVr1pRYl/du2zRfGNFlXoZMSlYgSO3XGCR29qNAZ1+WxRMf2wsaZqLGT/EFs3ygf3A0kGBsz9Y3BYDAYZkatVsNnn32GI0eO+D7rdrt4+umn55rIwWCYNrlcDqdOndq1h0J3Cul0GpcvX94RZ5DuJmjL3Ntvv+3Tred5PKXwbtuK9G/zFsBgMBgMo6Gc5JcuXVJ+fv/99+PAgQMzlspgmB3dbhePPvqoWSRMyMbGRuwpUQ23+dWvfgVA/RtGyWQS9913H0/pvJswCwWDwWDY4XzxxRcYDAZ4+umn8eKLLw5l47h+/TouXbqEt99+e44SGgzTgw5o7+bMMfMkl8uh2WzCcRy4rosbN27MW6S7Etd1sbm5iYWFBTz22GM8rfTnn3+OP/zhD3jsscemmohjWpitRwaDwbALaLfb+PWvf40LFy7wX/90HAcvvPACnnnmGfOG0GAwKFldXcWJEyeQzWZRLpd3TPr2uw3aXnTmzBlsbW0BuJ2yv1Ao4LnnntuViwTALBQMBoPBYDAYDAaDgqn9MrPBYDAYDAaDwWDYvZiFgsFgMBgMBoPBYPBhFgoGg8FgMBgMBoPBh1koGAwGg8FgMBgMBh9moWAwGAwGg8FgMBh8mIWCwWAwGAwGg8Fg8GEWCgaDwWAwGAwGg8GHWSgYDAaDwWAwGAwGH3NdKNRqNWQyGVQqlXmKsSvJZDLIZDIzqcvzvLu+r2q1GnK5HHK53NTqmGWfBdFut5HJZJBIJLCwsIClpSV4nhepjEajgUKhMFVdEb1eDwsLC1hdXZ16XfNkFvY3LWZpDzKrq6tYWFhAr9ebed1x4Xkeb0cikUAmk0Gj0YhURq/XQ6VSQTqdRrvdnpKku4cgf9oJ8eRuH0tnRbvdVsadSWNCo9FAIpGI7INB9Ho9LtNu9c25LRQajQY+/PBD/jPXhp3L5cuX8cEHH9y1fdVoNPDpp5+i2WzOW5Sp0mg0cPjwYZw/fx79fh/FYhFra2u4efOmdhntdhvXrl3D5ubmFCW9t9jN9mfsYXJyuRweeOAB3Lp1C61WC4PBAL/5zW8ilXHlyhWcPn0arutOScrdw072JzPviYd2u41z587t+LjjeR6uXLmCzc1NDAaDeYszPmyOtFotBoCVy+WZ110sFmde525C1s88+2pWAGDZbHbeYkwNx3HGsnvVM7tJV67r7gq73U06ldnNss+Ter3OADDXdSM9V61WWavVGrpWLpcZAN/1e5WdapPTGEt3S4yLm1n38bjzxt3um/fkGYVarbarv6qeNkY/dydbW1v4xje+EemZu8EWTp48OW8RDAYl165dAwCkUqlIzx07dmwa4hh2KSbGTZ+7YSwcl3tuodDtdrG0tDRvMXYsRj8G4m6whVqthrW1tXmLYTDExtLSktliZOCYGDd97oaxcBLGWijIh1trtRoWFhaGDmp6noelpaWhA1q1Wi2wzG63yw9ZZjIZdLtdALf3oiUSCf5HVCoVfi3oMAt9RnI1Gg08/vjjGAwGaDab/HPP87CxscHb0+12kcvl+OeqVSQdHEskEkin076DSWJ70uk0CoXC0D1hn4+i3W5z3RJ0YIYOszUaDS6froEH6UcmDv2MotvtolAo8P6Vn4/aX41Gg+ta97CgaF+y7als0vM8LvPCwgIKhQIKhcLQM3KfUVvHtQOqN8zPRDlXVlZ87VGhawtUP8mvOqw1iS3UajXfgTVdW69UKjh06NBQu8W6RbuQD3aPinGzsL8waOAie9rY2OA6oIOask2q4u8oHYj6lm0s6FBemD3E6duyHYhljPJF8fC1eJBY1JHslzQe6aLSqyg7xZeVlRUAd3w0zDdyuRyfFO7fvx+JRELZF2HxX+6HQqEQqY2e5/n8emNjI7IexDhK7Qga8xuNBpaWlni/kZ0tLCxoxxRVPJFjc6VS4fagapOYEEL+i3qgP2jeIzLKR4NinEomWVbSmahvUX6d2BBmR6K+dcaKMF2JdakOpAfFBPpMfF41vsvxh/QQNBbK4++4CUJ2POPsV2q1WqxYLPL9Yfl8ntm2zai4fr/PLMtixWKR9ft91u/3+f2lUmmoHAAsn8+zfD7PyuUyy2azDACzLIt1Oh1enlg+Qfs7xT1q6+vrzHEcXm+pVPI9Jz/TarVYPp/nshSLRdZqtdj6+jq/JlIsFpVtE/cIWpbFqtUqL9+27UifB9HpdHi7xXbV63Wuo2KxyMrl8lA/ra+vh5YdpB+SMU79jMKyLJbP5/nz1DdkD1H6q9PpDNXtui6zbZtZlhXabirPsizW7/eH7iV903WSg+rIZrO8rKA+o7aOYweM6fuZ2L6o+1hVtkDX6cxDvV5n9XqdWZbl0+skttBqtfjeTlGGKLYetB+4Wq0yx3H43nDqa6pnVIyblf0F4bou1zf1Ne1Zp/i5vr7OisUiq1arQ/eKdhymA6rLsiyuU5If0r56XXuIy7f7/f6QHYh7f8N8kcqS5aWy6vU6vy7eq0u5XGaWZXGZWq0W14V8FoHsOwpB+53pephPdDodZtu2Uj45zgVBupP7kWJZFD3Qs2J7yO5IN2IMpf4slUp8fFeNcapxXo4n5EtUhuhLjuMM2SaVIfpzp9Phcor3jUJ33sOYno+qYhzpWvR5sZ9kOFRCeAAAIABJREFUXdXrdT5v0q03zI46nQ7Xt05sGAXpmWzOdV3ePyTTqJhAY6UYe+h5URf1ep3rSB4zVPGZxgXSE7VX1u9uP6Mw9mFm0dgZY7zzGLs9ObBt2/eM3IFyGQQpVbxOjuRrgNR5FEBE5CA/aiKselaslyZzKjnoPipLDLr1ep0bXtjnOqj0QXqr1+v8Wr/fj3zgJ6p+RIfX0c8oyKFVkz3VNZ3+koM46UkO7EGBQJ4UURmiPCq9y2XJfTapHej6mShj3AsFERq0xUnfJLYwSgZdW1cNomRjcp9S/wTFJzHGzcr+RkHyivbjuq6yb+SBSlcH4iSbkPuZZA+zh7h9W9UukiXMF1XyVqtVZb1BY48K0r84YRbLDrLjKIQtFFQ+IY+lsnz0rE58oAWAHLfEfoyih6D2qPSumkxT3fLEc1TsCorNqjaJOqHxQIQm0bqxVXfeEzVOyfWr/JT6RT6UWyqVxooNOnY0KjboTpwdx/H1GbUlyKfEsqmPxGv0EidorqOzUKCXSPJ9QX27WxcKE59R+Na3vgUASCaTSKVS8DwPa2trcBzHd+8rr7wCAHj//feVZRDLy8uwLAsXLlyILM+jjz6KtbW1oa/Zn332We3n77vvvqF/J5PJoX+fO3cOruuO3Jby0EMPAQCOHj3Kv2Y9ePAgvyfs80n58pe/HCj/pKj0I6b90tHPKJLJJG7duoUjR47wr7cPHz4MAPjss8+05BHZt28fGGNYXFzkWyjefPNNAMAXX3wR2t4TJ04AGD4s5nkeTp8+jWeeeYZfcxwHr776Km9fMpnEE088MbLsSexgHD+LG1nX1Bek10ltQYdxbP3y5csYDAawbXtILkqn+Kc//WnofjnGiUzb/nQQ6yT5wnSho4Ner4dms+mz4zfeeIO3KUgOwG8Pcft2ELq+KJd3//33K+uNwrvvvgvgjm8ThUIBlmWh2WxO/UCkyif++c9/ArgdN5rNJg4dOjTU77QF6o9//GNo+adPn8aePXuG9Hfw4EEwxnDkyBEA09XDnj17hmzv4MGDcBwHg8Eg8hYxGbFNoh4J0qPIN7/5zbHqCpv3RI1TMv/1X/8FAPh//+//8Wtk27VabWh7zIULF7Bv3z7teqPaUVBs0KHb7WJra8vnw1ESAKjiixy/xuH8+fN8rklbVQG1nexmYj/M/PHHHwMAbNv2fUYOpRMg9uzZM1be2R//+Mc4cOAADh06hHQ6jVqthuXl5cjlBHH16lVks1mw29/G+P727duHZDKJer2OCxcuwLZtvi+T5Aj7fDejox8dKpUK9u7dCwA4fvz4RDLRPsIDBw7gscceQ7FY1H42lUrx3xugQejixYs4cODAUPB75513ANzeN5zL5dBut0P7cxI7iMvPpklcthA3lGkmSK64/XAS+5sWOjr4xz/+MZW64/RtFeP4YlxcvXoVwJ1Fh8iePXsAYGp61YHiRqvVUvb7+fPnQ8vQOUg9az0cOHAAQHyL7yBocirG1s8//xwA8MADD0xcvjjvmTROLS4uwnGcoYPO7777LtbX1zEYDHDx4kUAtye4L7zwAr9Hp9447EgXWqB85zvfGbsM6hvxN4NooRQ145gMnQf68MMPeey525ha1iMKFLMmmUyiVquh1WohnU7j0KFD/ABiXGxvb4fec/DgQdy4cQPlchm1Wg22bQ99yxH2+W5GRz+jyGQyOHPmDC5fvozl5WXlYKNLr9fDgw8+yOUqFAqR3xi+/PLLAMDflhw7doxfIxYXF/HRRx+hWq1ie3sb+/fv1zpEPqkdzMvPdJnUFqbJLBZScdjfNJn1YjJO3w5iXF+ME5pI7VSi/MjiJMxKD7PyqZdffhm2baNUKsHzPHieh1dffRWO4wwdmI+TSXz0Jz/5CQaDAR9T/vKXv+DIkSOwbZun2H3vvfeGvh2PUu+s7GjSugqFArLZLI4dO8bb9fOf/xyWZfnG8iisrq7iBz/4AX73u9/h1KlTsXxLsROJfaHwta99DcDtX4oMImxLBnB7UM3n82PLsW/fPpw/fx7VahVbW1v41a9+NXZZIo8++ihc1w3MNCE6VzKZxPLyMm7cuAHHcXDo0KGhBUvY57uRKPpRUavVsLW1hddeey2WbVMnT57EYDDA66+/PnYZ9K1Cs9nkGRWC3kIUCgVsb2/zbyF0stuMYwdx+dk0mdQWpsXDDz8MADh79qzvM8p2FBdx2N800NEB2diZM2eUZUTVU9y+HcY4vjgpFBeuX78eeM+8vkkD7mwFeu+995Sfq7L8yNi2jWazqYxRZBOz1sMnn3wCwL/VKW5SqRReeeUV/POf/8RXv/pVfPWrX0Umk4ntDbo474kjTj355JOwLAvvvfcearUann/+eQC3t6e6rotarYZUKjXkjzr1xmFHutAi8K9//etE5ZTLZViWBcdxkEgk0Ov1cOnSpbG/Uej1ejhx4gSKxeJdu0AgYl8opFIpZLPZoVUsQStC1epVpN1uw3VdbtTAnUmPOLlQ7UeU3xwVCgU4jhNb3umnnnoKAPDd7353aALU6/Xw1ltvIZVKod1uD7U9mUzitddeA3BbB2Gf72Z09DOKTz/91HeNvtodB9VkVLVfMQx663DixAm89NJLvs9lu6OJ4d///vfAMiexg6h+RnoYp+3jMqktTIu9e/fCsiysrKwMDWie5+Ho0aN8a0QcxGV/caOjg1QqBcdxsLW15Rv4K5VK5ElZ3L4dxDi+GBfPPfccAOAXv/iF7zNatIjQBHdWL4iSySSy2SyazaYvjWOlUtF6M08T2aNHjw5db7fb3Laj6IG2lMjbQoK+jVTp6sKFCygWi1NfgDYaDbzzzjs4f/4832Zz6tSpWOqV5z1xxKlkMolCoYBms4n33nuPn4F78sknAQCHDh3ifUXo1BuHHelCsorbf6PS6/Xw+OOP4/z587h16xbfHjXJBF+1dW63v+gNZNxT0OIJfTmlGqU2U6VGE0/J06lz27Z5pga6Tz6VT9kS8vk8T3MmZjsol8s8u0U+n/dlJxEzQVCdlD5ObI9Yr5iWVcwcQCnn8K/T/KosBGJbKS0ZnfwP+zwMykogy0VyBOlYN/WdSj+UpUA8zT+ufkZBfeo4Ds8ARM8Xi0WekUS3v0S5qT1iWjQ5E9UoPQVlGSKdkQ2SfGLmCFWfTWoHun7G2J2sD1HsgNol2wL1kVyWKvXeJLZA7VHVpWvrYoYRMaMUxRO6P5vN8jhCjIpx87A/kSDfIx2IqQ4Zu9MPYkYfHR1QedRW8kex3br2ELdvM3bHDuQMZKN8MSgmko/I9VJ/iWPIKKjP5bTAcp+IbYqSvlrMLFOtVlm1WuVjn1yWqq1iSk/Lslg2m+X9r4ModzabZeVyWdk+XT2Imbrq9TqrVqtDtlEqlXifky1S/1KZcpaeIH9SXRdjs1iGKt0xyUTZwCjLIqWj1WGcec8oHw2KcXJ98phAqT1V6MaGMDsi2eQ+D0rTGgTZEo1tYupfukZ9p/IDMU0r9RvFs6DMTapsRuJYSBnFqP5qtcrb5TgOW19f53arkmk3MdZCgRot/sm4rsuVJgYBGTFv9qj7GLtjLLZt884l4yUjoYmWaORyeeQEtm2zTqejbA8ZuPgnTyIoWDqOMxQkKAczOZEYLHU+H4VKLtU1CuCqe8MYVz9i2aP0E4bczzQhHqe/xAHUcRxfPu5+v6+tJzklqoht27y9VLa4IFCVP4kdEDp+JtdN8ukg20KQrsb1lVGo6lK1J8zWxd9DkNMfUqywbXuob0fFuHnZHxFUl27fyL9JEaQDotPpDN2jWkzp2kOcvh1UxyhfDJJ3nDFgFOvr61wGMf/7KL2F9TshLl5ocaXrE2KfUtywLIuVSqVIcUfMya9qn64eiGq1yl96UJtoMqdKwyvGPPHFYJBuxd9Q0OnjIP+nybFoX+PYR5R5j46PBsU4QnW9Wq2OnLjqxoYgOxonHo2CFvyiT5NNUf8H9QctbsW5oUoGud9F2eSxkK7JdktjOs1Rx23vTiLBGGMwGAyhpNNpXL58eSb7qw0Gg8EwTCKRQDabjTWrThSWlpZw5MgR5ZaVRqOBt956a26yGYKp1Wr47LPPePpekW63i6effnpHJ96YN1PLemQw3E1sbGz4UqIaDAaD4d6g0WigVqsF7mv/93//97knkDD4od+v+fa3v638/P777+fpdQ1q/m3eAhgMO5VcLodms8kPw9+4cWPeIhkMBsM9CR1k3d7ehud5M39p8/e//x2DwQC5XA7f//73h35o7dy5cwBu/xihYWfxxRdfYDAY4Omnn8aLL7449HsM169fx6VLl/D222/PUcKdj/lGwWAI4NFHHwVwO3PEpUuXzLcJBoPBMAcqlQoeeeQRALd/8O2rX/1qLL/sHoUjR46gWq0CAH70ox9h//792L9/P37961/jP/7jP8wiYYeyuLiIVqsFx3Hw5ptv8n77n//5HwDA22+/bcb2EMwZBYPBYDAYDAaDweDDfKNgMBgMBoPBYDAYfJiFgsFgMBgMBoPBYPBhFgoGg8FgMBgMBoPBh1koGAwGg8FgMBgMBh9moWAwGAwGg8FgMBh8mIWCwWAwGAwGg8Fg8GEWCgaDwWAwGAwGg8GHWSjcI9DPmC8sLMxblEA8z8PGxgbS6TQqlcrU68tkMshkMlMpu9frYXV1FQsLC1P7YaBer4dCoYBEIoFEIoFCocB/vZT0mEgkuD4nae80dXUv0G63USgUkMvlxi7D8zzUajVkMpmZ+Me9RKPRQCKRQKPRmLcoO4J2u42lpSUkEomplG/seGcyagym8azX60UqU7evZz3+R6XRaEwcw3crZqFwD9Dr9XD27FnUajUMBoN5ixPIxYsXcenSJbiuO29RJsLzPFy5cgWbm5tT03ev14PjOPjZz34GxhjW19dx4cIF/PnPf8bq6iouXbqE7e1tuK6LdDqNN998cypyGMJpt9s4d+4cNjc3Jyrn8uXL+OCDD7C1tRWTZAaDn263i5s3b2JtbW0q5ddqNWPHO5S4x+BGo4EPP/xQq69nOf7ncjn+gi3sL5fLod1u49q1axPH8F0LM9wzZLNZttO7vNVqMQCsXC7PW5SJKZfLDABrtVqxl10qlZht277r/X6fAWDVajX2Og2TAYBls1nt+13X9fnB3eQfhp1N0HhRrVYnjmnGjncucfdNlPJmZRfZbJbV6/WhawB89r6+vj4Us6PG8LsF842CwbALuXr1KtLptO/6xx9/DAC4//77Zy2SIWZOnjw5bxEMBh/Hjh2btwgGw0QcPnwYBw8eDL3vyJEj+P73vz8DiXY2ZqFgMBgMO4xarTa1rR8Gw7gsLS3t+q2hBkOhUNC+98iRI1OUZHcw0UKh2+0GHqYkPM9DpVIZOli5sbHhK6vRaCCTySCRSGBhYQFLS0tDh2booAsdiul2u3yfWS6XUx6wkeutVCrwPI9/3uv1+D3tdhvtdpvLkMvl+L10iCeRSGB1dZU/L+9lEw/giHvgdA7miO1PJBLIZDJDB+vocCzJ2mg0eNuWlpZ85Xmexw8vy+2JgkqHMnRQU7QDuT9keTKZDGq12sSyqNok9qNqvyHdE3S4O6w9clvIXsfRr4xKT6IdkF01m000m80hG0skEti/fz8AYP/+/bG1d9SzKr8lPYzjs3TwjcpbXV2F53lot9u+vhQPiQddH0fnqv6s1Wr8IBvdT3aoqk+OjWLcCKNSqeDQoUMAgJWVlcAYEqbPMFsaRbfb5c96nsfrEZ/XjQ1hvigeEux2u0in00OHJnXaUavVuCyZTIbvK9b5XLZTmbCxKWpsDmKaeqD4IOodiDZO5XI5vnil+KLra7IO33///aHP5YP6tVoNCwsLPIFCmI+K7SO55GviwWyx3e12O7J/ByH7vewXYe1UlaGaV43S86Q2pEK2cbE9UcbCbrfL7SCTyWi3C9CLN7NCx0ZGjY27jnH3LHU6HWbbNt+r2Gq1mGVZzLIs1u/3+X2O47Biscj6/T7r9/ssn8/79lCXy2VmWZayLNd1+TV6Np/Ps2KxyFqtFltfX+fXRLLZLHMchz9P9zmOw++p1+vMcRwGgBWLRVYul1mr1WKlUolfK5VKbH19nbVaLX4v7W1zXZdfU+2pcxyHra+va+lSLMN1XWbbNrMsa0hW27Z9shaLRQZgqJ5+v88cx+Ht7/f7/L4oXV4sFof6jsoQ21qv1319B2Bo/3y/32eWZSnLKpVKQ3UG7VHU6U/V851Oh1mWxQCwTqfDr9XrdaU+dNqTz+eZbdtcFjqLIPd11DMK1G9UjmhfchnZbFa5V5LkFe+fpL2jnq1Wq8o+Ibmi+mypVBoqj/T3/9s7v9c2riyOfw37um3H6tMumweN8xAa8NKMYyhKHwrxON5QstAiJSzB0CVBztKHpbV35S6h1A0rNe2DWWwnOOCnSKYpCwtS7AYaiEQhjbPYNGH3wRIhLfskWWn7B9x9cM/kzswdzR398I/kfMCESDP3nnvuueeeq7n3TDqddsqjvvTqg85mUB9HIaw/19fXnc/InxWLRUd38jil62V7l/tRd39r0Digz8P0GcWWvFSrVadtcl2yPDq+QXcsymOZ7Jrk1GkHlUtl5vP5SN8Xi0XHTr361pmbovjmIHZKDyobpPlO5XO9Y76dc1ckB8kuj2OqU9aXbdvOmKT6w8Zo0Jwi+zM5LhFie37O5/ORx3crDMMQyWTSF+9Qn4S1UzeuUtENG5J1Rn1Tr9ddNi5fqzMXyj4rmUyKbDbryCXbq6puQsffdIpOfKRrI2Fz436j7YWCbdu+A5NkJNR55GRlA6egQzZW78JBiKfORVYsGZE3uCRj995LnUSojItk9h5soUEhy64yYnK+FMwQ9XpdedhUBZUrDxiSS/WZLCsFSLKeMpmM0rHIDklHJpX88mCiBYB3sFqW5Ro46XRaWZbK8ah0HKU/yTHL0CANmwh120MO3qsX72dRJ9VsNuuzI9KH18FEWSjI97TT3lb3evtEnphkecLGrEruarXqTLwE9aXXX9CipB10+1O1MKUgS5abJmkZ8nPdWiio9Cn3WRRbCoL6Ug7qqZww3yBEtLHoDeaoLp12ZLNZZb9Qn4R9L5cpyxVlbtL1zWH0Ug9UvleeVj/OdLpQoDHsLZt0qFpY0rijRTbJEjZGSd/e62iekH1VvV736UF3fAdBPlEVJAcFzqp2hsVVYXRiQ/JnqkWzVxc6/eJtr7c81bVeu9DxN50SZaEgQzZCetadG/cTbW09ajQaWF1dxenTp12P9aampgAAX331FQDg6tWrOHr0KGKxmHPv2NgYhBDOvq9r164BAA4dOuSqI5VKwTAMrK6u+h6pv/jii67/y+UDwKeffgrDMBCPx12fU51Xr171temFF15w/Z8OinrL9jI4OIh0Oo35+XmXnNeuXcO5c+da3kskEgkIITA4OOi874DSWf70008tZVXJNz8/79O73CYdbt68iWq1GvjotlKp4NatW2g2m3jttddc9967dw9bW1sAtm1lfn4elmX56nj//fcBwPcY2kuU/nzy5Inv/ldeeaVl+YROewBgZWXF2TZFj5GD6o7C559/jvn5eZe+aSvR6upqR2Wr0G2virt376LZbMI0TZe8JOfXX3/tuj5szJINJBIJ57N4PI6trS3XFrXz58/DNE3fgcqZmRn86U9/ailzEFH60yu3t10bGxtYW1vDG2+84frca7udotKnnIq3m7Y0ODjo+lfHNwDRx+LAwICjX6pLpx2HDx/G2tqaayviiRMnnHLDvg+inbkpzDfr0Cs97AZBPqZVsoVXX30VwLb+aNzojNFYLIZkMonl5WXXFg/DMAAAN27ccD67ceMGxsfHfXWHje9WxGIxbG1t4fz5886Wa6rjhx9+CG2nblylQ7s2FJUovpPaS0xOTsIwDHz55Zct69D1NztFkI1QrBZ1btwPtLVQoMwq5XIZYvuphOtvZWUFALQOPd2/fx+A2nEcPXoUAPC///0vknxra2swTdP3OQ2Ybh/Geu+99wAAmUzG+ezq1at46623tMugPW8jIyN4/fXXkU6n25KlUqmg2Wz6ApWo3L9/H7ZtK/tXCIFEIoHHjx+HlkO2ouoPChrCXuASpT9pYpHL/PHHHwEABw4caFmPTnsI2od8584dLC4uat/XirW1NWSz2UCdd5so7fXy4MEDAAiUdXJyMlJ5UV7iMzMzg2q16px1qtVq2NzcdC0yotKt/qTJwBsY7TS9tCUd3wB0NhajtGNsbAzZbBZTU1M4ePAgcrkcDh065MgR9n2rdgLdnZvapRt62A3++c9/AkBXZNAZo2fPngXwdFHQaDTQbDZh27brB6XFxcVIB1qjkMvlMDw8DGDbV+miG1e1S698Qie+8+jRo6HvGtL1N3uFbs+Ne4GODjN///333ZLDGSTdYidf5hKPx5FOp7G8vOwcjBoZGdH+FbFWq+HgwYMAgM3NTaRSqUi/ZKh49OhRR/eTLN2CJt120e3P9957D6ZpIpPJoNFooNFo4OLFi7Asq2sTw/T0NP7whz/giy++wNzcnLNg6Qbd6LedJOpbOrtBKpWCaZr461//ikajgcuXL0eakL30oj+76RvbpZe2pOMbujUWddoxOTmJarWKVCrlBMryQcmw71vR7bmpXbqhh92iUz+hO0bHxsZgmiY++eQTANsLhjNnzmB8fBzVahWFQgGVSgUjIyMdyRPE0NAQPv/8c9y9exeTk5Ntpanupe/otk/o5Vwo081YZKfYjbmxV7S1UKBHsUtLS8rv6Zc+0zSxurqqPOlNj6somH748GFgfVFXjPTrc5CDtG07Unk60FOFmZkZfPrppzhz5oz2vZcvX0az2cRHH33UsRy//OUvAWxvH+mEI0eOoFqtBp7mr9Vqzq+CN2/e9F1Tq9VQqVTwq1/9CgDwzTffBNYV9vQjSn/G43G8//77ePLkCV5++WW8/PLLGBoa0vo1Rqc9tVoNly5dQjqd7rpTtCwLhUJBOV5UmcI6Rae9QRw+fBiA+3E+QZk9okB+QJWBQ1XWzMwMms0mPvvsMxQKBRw/fjxSfUS3+5MW+N9++23HZXVCL21JxzcAnY1FIko74vE45ubmsL6+DgD44x//GOl7L72Ym9qlm3rYSV566SUAwH//+9+2y4g6Rs+dO+fY5+3bt5FIJJztYktLS7h+/TreeeedtuUJolAoYG1tDR9++GFb285046p26bZP6Ibv3NzcRDKZbHmNrr/ZK3R7btwLtLVQiMVisG0bq6urvpRPuVzOmSzJAC5cuOC6v1KpOHv2KKCmXwBkNjc329qCQ2cDvMZPBqXam9gp9FSBth5EmUBUhq7a06jD4OAgTNPE2tpaRwZJe1vffPNN1wCt1WqYnZ1FPB53grNLly75gvhMJoNEIoF4PA7bttFsNn3y0C8nYVu0ovRnqVTC4uIiVlZWnEd9c3NzWo5bpz2qrQbdSnn29ttvo9ls+lJdVioVnzybm5vKekmntMWjFTrtDWJ4eBiGYWBqasrVL41GAxcuXHC2Zuhy8uRJAMC7777ralej0cCdO3d819NTBZqo2t0P3u3+JJ3Oz8/v6i+5UWwpKjq+AehsLEZpRy6Xc31H58ZoS2LY90H0Ym5ql27oAdj+UcX762yrhVCnnDp1CgBw8eLFtsdV1DFK88n4+LhTP7DtM+hcSbfPDAHqrZw6fpjQjavapds+oVPfWalUUK1Wne1iQej6G0qjvdt0e27cE7R3Btqd6s4wDGHbtjBN03V6njL/4OdT9XTq3rIsV0YeOR2pnP7Ke503XaK3DjlDEGVV8aYC8576p8wc3vSiVKZ8cl3OdqFKVRaUJSMMan8ymXTSp8nyUwYAOjUvl09Zl+QsB3I6S0rVRxmoqEydk/dySlXK5ALPqX3KYmIYhshkMiKbzQrTNF0yUuYLVZrBoAwP3gwJuv1JMlJWINu2HblkuSkzgdduwtpDmTVI9nw+79gQpZ6j8kgWnfSIVDa1k9pA2Wxke6M+98ouxFN79uqv3fa2upfGA9mfbdu+DCdRxizJbpqmyGazIpPJCNM0A1OeBmXDIp3pjEPd/qRsHF6fpPIfNJ6pTDklIn3mldmLnCmkWCw6OpV9RSt96tpSEJSi2TveCR3foDsW5RSWXr3otIOy/VCZJDtlhgr7nq5RjRvduUnXN7ei13qQ+01OBS6n1JTTjarGvJyBh8ZLGCS3ZVlOSknSF6Uo9faBrK8oPpegtJ0y1B8qmaOM7yBo3qV2ymlAKdV6q3aSjGFxVSs6tSGvfDLe+Uy3X+RxQFnBaP73zt9Bdev4myh95YXGgGpOJah/veNZVa/O3Lif6Ci31Pr6uqMkCjS8hi/nEpZzi3tZWFhwJibVdWQY8h8NCvlPzv1LaULl4MPVeM+9tm0Hlqmq30uUlKje+2THKeeup0WJSlYaVF6dCOHO7U0DlFKZRUnPRYEjyaa6N5/Pu67xppoVYnvSIjsIuq6VjnX6U4injpbkUfWlqo/ldoW1J5/POwsfcv7e4FTVXzpQICLnsJcdvkpHQWOhG+0Nu1d+F4lpmi5nGXXMCiFcfZxMJlu+FyEoJSr1v+5YDOvPoHHWqo/lhblt2653K4QtEgg537rsI8LkoP4Js6UgwvpIbmMr36AzFlv5MCKsHV7/rFqstvreq1fvWA2bm6L6ZhU7oQcqwzvXUPrJfD4v6vV66LxK492bojcI73t8KAc9+fAg25bR8bky8uJaxpvaspXu2/HhtLAkfdKPZPSDh04coRNXqeiGDQXJF2QPuv0iv1NHNc+0qltuXyt/Q7pXxR+tUPkmVd1R57JWc+N+o0+IHqRTeU4pFAr49ttv8fHHH++2KM8tExMTOH/+vHLPZKlUwuzsbMfZI5i9wcTEBE6ePImxsTHl9wMDA/vyENyzAo9FhmGY/U9HWY+YpzQaDXzwwQc9OSTF6FEqlVAoFAIPVv1kF6VLAAANH0lEQVT617/uOG0sszfY2NjAl19+GbhIuHLlCmZnZ3dYKobgscgwDPNs8IvdFmA/U6lUcOzYMRiGgf7+fpw7d64nh6QYPb777jvnsNbvf/9718udKLMPP+3Zv+RyOUxNTcE0TWxtbeFf//qX75parYZr167hxIkTey6/9vMEj0WGYZhnA9561AG1Ws154/Bf/vKXffkijWeNQqGApaUl15smk8kkTp061bMX7DA7Q6lUwu9+9zuYponZ2dnApwnM3oDHIsMwzP6HFwoMwzAMwzAMw/jgMwoMwzAMwzAMw/jghQLDMAzDMAzDMD54ocAwDMMwDMMwjA9eKDAMwzAMwzAM44MXCgzDMAzDMAzD+OCFAsMwDMMwDMMwPnihwDAMwzAMwzCMj2duoTA0NIShoSHld41GA9PT0+jv70dfXx+GhoZw5coV9Pf3Y3p6eocl7T2FQgGjo6MYHR3dbVF2nFKphFQq9cy0vVar7Xk7rVQq2jp/nm2TYRiGYfYLz9xCoRWjo6M4cOAAtra2UC6X0Ww2sbi4uNti9YRSqYTHjx+73or6vFCpVPDgwQMsLy/vtih7lkqlgr6+Pu2/SqUSWt7Dhw+1dN4r26xUKhgaGnJknpiYQK1W62odDMMwDPNcIZ4TisWiACCq1epui7KjABC2bfs+T6fTuyDNzhLUdkaIcrksksmkazxks1kBQGSzWeezarUqLMsS5XJZq9woOu9m/5TLZWEYhkin0yKbzQrLsgQAYRjGczfmGYZhGKZbPDdPFB48eAAAiMfjuyzJ7lOr1TA/P7/bYjC7zKVLl0LHQzwe3xdP3f7xj3/g9u3bmJubw+TkJO7du4dkMolms4nLly/vtngMwzAMsy/5xW4LwOwsjUYDyWRyt8VgdplEIqF97eDgYA8l6ZxGo4FTp0755Lx06RKWl5d5+xHDMAzDtEnbTxQajQZSqRT6+vrQ39+PVCqFVCoFYPsX6ytXrmBgYACVSgWlUgkDAwPo6+vD6OgoNjY2fOXlcjnnmoGBAeRyOd81dFiS9iCnUilXEFCpVDAxMYH+/n5XuX19fZiamgIA514qv1AoBB7ALBQKzp5nOkjaaDRa6oTuyeVyKBQK6O/vdx2u3tjY8LXBqw/5wPXo6Khz/+joqE9+uU1he8lrtRqGh4extrbmui+s3laUSiXXvvChoSGUSiVXndS3lUoF09PT6Ovrcx3Klcvo7+/HxMRESz1HlbfRaGBiYsKxLZWOVP0iX0d2JOvZ+5msS7mvKpWKy8505PGistNarYbp6WnlOJuYmAgtMyryGA2yXUL2DwMDAygUCm3VE+QLZGKxmON7ZOhpyUsvvaRdN8MwDMMwEu3uWUomk84+92q1KmzbdvYbF4tFkUwmBQCRyWSEbduufcOmaYp6ve6UlU6nRTqdFvV6XdTrdZFOp317pYvFojAMw9krXS6XnbKEEGJ9fd05h6BqFu2/limXy87n3r3SmUxGWJbl7G+m61rt7S+Xy47stm2LZDIpTNN06l1fXxemabraYBiGMAzD0cfCwoKwLMvRRSaTccm9sLDg0029Xhe2bQsAvr3kqrbRtTJh9apYX193yVKtVoVpmsIwDOeaYrHo9Ltt2yKdTgvDMByZ8vm8S8/UvrC96zryAhCWZYl0Oi2KxaJjQ7J8Qjy1pYWFBacdJHOxWBRCCJddZjIZ371emxZCCMuyRD6fF+vr64796MjjJchOi8WiY1+0N1+2QWqPLqozCgT1i3f8JZNJ13XUxmQyKbLZrCMLAJHP533XevtZxxfoUq/XlfUyDMMwDKNH2wsFOYgS4mmwSgQF1hSAURBTLpedYN9bPgV+9XpdGIbhCxYsy/IFWaogWJYnqC2y7BQEyUF3tVoVhmH4AiMv3gCqXq87QbBt276gxRuc2bbtCkSpnd7yvbqgctpdKITV26qt6+vrPjlUn8mBOAWCqsOmQYueqPJS0CpDCwpZPtM0fXZarVadw7C0AKDA02sDFMzK7fCOhzB5dA4Lq/qSdOsdizqLLS+tFgrZbFY51ryfqRZNZCeqa73jLswXRCGfzyvLYxiGYRhGj7a3HlmWhYsXLzrbJmKxGN544w3fdWfOnHH9/8MPPwQA3L59GwBw8+ZNVKvVwC0clUoFt27dQrPZxGuvveYq6969e9ja2mq3CYFcv34dgHsfdzwex9bWlvYWildffRXAtl7i8TgajQZWV1dx+vRpVztpS9RXX30FADhy5Ajm5+dd9bz99ttdaVcr2qk3kUhACIHBwUHUajVMTEzg73//OwDgp59+8l3/yiuvANjWZSwWw927d9FsNmGapksnlDbz66+/7ljeWCzm+v+LL77okq9UKqFarfr2t8fjcecw7K1bt5yykskklpeXXVujDMMAANy4ccP57MaNGxgfH9eWp1NeeOGFwDq6weTkpDPWaAvg6uoqms2m79qBgQGXDIlEwtFl0FYlQM8X6NJoNPDBBx/giy++0L6HYRiGYRg3bS8UKBPKsWPHMDo6ikqlgsnJydD7hoeHAQBPnjwBANy/fx+2bUNsP93w/SUSCTx+/LhdMduiF4cf//Of/wAAyuWysp0rKysAgD//+c8YGRnB6dOnnb3dOnrtlHbrpf32IyMjeP3115FOp7XrpExUQX3fqv5u6Ylk+M1vfuP7jhZ7sv2dPXsWwNNFQaPRQLPZhG3buHr1qnPd4uKict/8fmZjYwOjo6OYmZnB2bNnYdu29r2kS9UCktDxBbr87W9/w8zMzJ4/iM0wDMMwe5m2FwqDg4O4d+8e8vk8Njc3cezYMa0DlKpfOzc3N9sVY9/x/ffft/w+FouhUCigXC5jYGAAp0+fxtDQkNbh3k5op95arYaDBw8C2O7DVCrV1i/k7SzMuq0nWjCEMTY2BtM08cknnwDYXjCcOXMG4+PjqFarKBQKqFQqGBkZaUuOvUqpVMJvf/tbjI+PY2VlBWNjYz2ppxu+oFAowDCMZ26hxjAMwzA7TcfvUUilUtjc3EQ6ncb8/Lwr440KCgqPHDni/FutVpXbCkqlEmq1Gg4cOABge2uCqrwoWxJ0oGwpqrZEyd4ic+jQIQDA0tKS8vsrV664/p9IJLCysoJ8Po+1tTV89tlnbdUblSj1Xr58Gc1mEx999FFbdR0+fBiAe8sOQRmkuimvCrKtf//734HXeLe8nTt3zrHZ27dvI5FIIJVKwTAMLC0t4fr163jnnXciybHXeffdd2FZVtvB96NHj2AYRsunAjq+IIxCoYA7d+7g448/bktOhmEYhmGe0vZCwfv0gILF7777zvX5jz/+6Pr/N998AwBOIHXixAkAwJtvvukKEGq1GmZnZxGPx3H8+HEA23nRvXucM5mM1paER48eAYDWL84nT54EsB0cydc3Gg3cuXMn9H4VsVgMtm1jdXXVl/4zl8s5v8R79ZpKpWBZFqrVKoCn5yaoPUSrQFeHsHpVqAK3H374QbvO4eFhGIaBqakp10Kp0WjgwoULOHr0aFflVXH8+HEYhqHMt//o0SOYpumzr7feegsAMD4+jlOnTrlkWF1dRa1We+Ze7KfSq+7TG1r0hW1L0/EFraBFwtzcnLJ+ubxWZyUYhmEYhvmZdk9B4+fsKJTdhLKieNOJWpblSwfqzVYjp1C0LEuZ9YbSM9L92WxWmKbpyiJEWXTgyWpTr9edNJLelJFB6S0pvatpmiKbzYpMJiNM03SVq4LanUwmfeky19fXHfkoRahpmq7ML5RWlfRI8slZbSgFaT6fF8ViUWQyGSd7TjKZdK4Nahvpl+6l9LZh9XqR66S0nHJWK7IP0qUqm04+n3f6nnShynDlJUxeSpUb1K+yHdC13jSthmEE9jelvpWhdLGqdJwkH6V0bSWPirC+lOskOVQpW4OQx4gqsxf1azabddIf0/XFYtGpn9K9LiwsOBm/LMvytTuoPTq+QEU+n3fsxvtnmqYrI5XKRzAMwzAM46fthYJpmk6ggJ/THKpSYqbTaec60zQDAyKa0L2LCxlKd0jXyEEsBR7yn5x/Xvc7mUwm4wQVyWQyNLCgoCaoPCG2gzgKDmnR4w0cKSgjnXmDdXofg7wgyWazwrZt51pV20intGChxYZuvV7k9zdYluV6l4Vt206aTvlPlbJTftdCKxuRaSVvUNu9n8mLkXK57CovrL+LxaJyMaNKKasrT1A60yA7Vd3fqt+DCBojcvtUNkcLRXnhX61WnXdlyAttHX3I34f5Ahn5/SmqP2+fUFplb1pehmEYhmHc9AkhBHpALpfD1NQUyuVypGwlDMMwDMMwDMPsPh0fZmYYhmEYhmEY5tmjZwsFOlz78OHDXlXBMAzDMAzDMEyP6MnWI/ltqgBg27bzQjGGYRiGYRiGYfY+v+hFoT069sAwDMMwDMMwzA7BZxQYhmEYhmEYhvHBCwWGYRiGYRiGYXzwQoFhGIZhGIZhGB+8UGAYhmEYhmEYxgcvFBiGYRiGYRiG8fF/3oNoEI1R+SEAAAAASUVORK5CYII=\"\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2\u003c/strong\u003e Regression results of the factors influencing the coupling\u003c/p\u003e\n\u003ctable border=\"0\" cellpadding=\"0\" cellspacing=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Geographical location\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Resource-based city or not\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eOverall\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eEast\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eCentre\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eWest\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003eYes\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003eNo\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eCXT\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.0294***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.0342***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.0164*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.0433***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.0241***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.0350***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e(-6.2002)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e(4.2797)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;(2.2851)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e(4.3934)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e(3.3382)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e(5.4511)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eJSC\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-0.000262\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.000375\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.000249\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.000468\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-0.0000783\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e(0.2437)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e(-0.3309)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e(0.5139)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e(0.2282)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e(0.6372)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e(-0.1204)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eHJG\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;-0.0389***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-0.0652***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-0.0155\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-0.00315\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-0.0542***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-0.0174\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e(-4.2059)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e(-4.3473)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e(-1.0942)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e(-0.1525)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e(-3.8916)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;(-1.4016)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eGJH\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.0887***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.0932***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.112***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.0784***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.0988***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;0.0753***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e(14.7801)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e(8.0245)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;(12.2764)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e(6.8871)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e(12.4524)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e(8.4153)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eZFL\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.419***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.404***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.349***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.397***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.393***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.444***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e(23.1616)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e(9.2101)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e(14.1768)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e(10.8072)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e(16.0560)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e(16.4902)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eJFZ\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.109***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.126***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.0757***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.139***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.0824***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.131***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e(29.6606)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e(19.8140)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e(13.6906)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e(17.8252)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e(15.5143)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e(25.5706)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eKJP\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.0026**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.0076***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.0012\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.0008\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.0016\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.0022*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e(3.1475)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e(3.5213)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e(1.1544)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e(0.5509)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e(0.4029)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e(2.4933)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eR-squared\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.6390\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.6478\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.6380\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.6903\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.6093\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.6674\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eObs\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e2750\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e1090\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e1070\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e590\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e1110\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e1640\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eNote: \u0026ldquo; *, **, *** \u0026rdquo; indicate the statistical\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003esignificance level of 10%, 5% and 1% respectively.\u003c/strong\u003e\u003c/p\u003e\n\u003ch4\u003e\u003cem\u003eRegression results\u003c/em\u003e\u003c/h4\u003e\n\u003cp\u003eUsing the above model, the study explores the heterogeneous effects of factors on the coupling between\u0026nbsp;digital economy and ecological efficiency in China. The specific analysis is as follows.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eIndustrial collaboration\u003c/em\u003e\u003c/strong\u003e. The\u0026nbsp;overall\u0026nbsp;regression coefficient of industrial collaboration on the coupling of digital economy and ecological efficiency is positive, and the P value is less than 0.01, which is significant at the 1% significance level. This shows that while the collaboration of producer services and manufacturing is enhanced with the help of the digital technology, it simultaneously promotes ecological efficiency. The synergy between industries permeates the interaction, which contributes to the synergy of the two systems. From the perspective of geographical location, although industrial synergistic agglomeration has an economic effect on the east, centre and west, it has the strongest positive effect on the west and the weakest in the centre.\u0026nbsp;The reason is that the west is relatively backward, the dividends of the digital economy are relatively more significant, and the cleaning function of the producer service industry for the manufacturing industry has been significantly improved, resulting in a relatively prominent positive effect on coupling. While the central cities are actively accepting the transfer of traditional industries in the east, the role of industrial synergy has not been fully reflected. In terms of resource types, both resource-based cities and non-resource-based cities are significant at the 1% significance level, but the coefficients of non-resource-based cities are higher. It can be seen that without the constraints of resources, the industrial synergistic agglomeration of such cities has more advantages, so it is more conducive to coupling.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eTechnological innovation\u003c/em\u003e\u003c/strong\u003e. The overall regression coefficient of technological innovation on coupling is positive, but the effect is not significant. As an indispensable factor affecting coupling, technological innovation has not effectively promoted the synchronous improvement. Innovation can accelerate the development of the digital economy through technological improvement. The growths of digital economy help to quickly transform science and technology into the production process, promote the improvement of resource utilization, and reduce pollutant emissions in the production process. Therefore, this factor should promote the simultaneous development of the digital economy and ecological efficiency.\u0026nbsp;The reason is that digital economy is mainly on the basis of extensional expansion, and the effect of \u0026quot;learning by doing\u0026quot; is not obvious. The breakthrough process of the innovation possibility boundary is relatively slow, and the large-scale expansion of digital capital has not only failed to realize the effective replacement of factor innovation and energy demand, but has also solidified the existing energy consumption pattern and aggravated the energy rebound effect. Therefore, there is an urgent need to bridge the digital divide and technological deficiencies. This also reflects that green technology research and the development of the digital economy have not fully matched in China, and key technologies and green innovation are seriously lacking.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eEnvironmental regulation\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e.\u0026nbsp;\u003c/strong\u003eThe overall regression coefficient of environmental regulation on coupling is negative, the P value is less than 0.01, it is significant at the 1% significance level, and there is a relatively obvious inhibitory effect. Furthermore, it also has a negative effect on the eastern and resource-based cities with developed industries and large energy consumption. This may be due to the fact that environmental regulations often cannot be adjusted in time with market changes.\u0026nbsp;In the context of the digital economy, specific regulatory measures need to be further analyzed in detail, and the strength of regulation and spatial spillover effects must be accurately grasped. In addition, due to various constraints such as budget and investment use, the government may have certain limitations in the process of intervening in environmental governance. It can be seen that only appropriate environmental regulation policies can actually help companies or have obvious incentives, and can promote the benign interaction of economy and the ecosystem.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eIndustrial upgrading\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e.\u0026nbsp;\u003c/strong\u003eThe overall regression coefficient of industrial upgrading on the coupling of digital economy and ecological efficiency is positive, and the P value is less than 0.01, which is significant at the 1% significance level. This reflects that new business formats derived from digital technologies. For example, 5G, cloud computing and other digital technology accelerate the transformation of traditional manufacturing into mid-to-high-end industries, thereby contributing to the industrial upgrading. The dividends brought by industrial upgrading have improved the ecological quality while benefiting economic growth. For the geographical location, the implementation of policies such as the rise of central China has continuously improved the market environment, expanded the positive external effects of the digital economy, and created more room for ecological quality improvement.\u0026nbsp;The digital industry in the west is relatively scarce, and the development level of the digital economy is far behind that in the east. As a result, the green efficiency improvement effect of the digital economy has not yet been exerted. From the perspective of resource types, in view of the development characteristics of resource-based cities, it is necessary to get rid of resource dependence and realize industrial optimization, so the trend of industrial upgrading is more significant. The positive effect on the coupling of the digital economy and ecological efficiency is also more significant. However, the dividends of industrial digitalization have yet to be tapped.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eGovernment support\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e.\u0026nbsp;\u003c/strong\u003eThe overall regression coefficient of government support on the coupling is positive.\u0026nbsp;In addition, the P value is less than 0.01, which is significant at the 1% significance level. Compared with other influencing factors, this factor has the most significant positive effect on coupling. This directly reflects the crucial role of the government\u0026apos;s macro-control to ensure the high-quality development of China\u0026apos;s economy. From the perspective of geographical location, the government in the east has relatively strong regulation and control, and the financial support is relatively good. This has led to the region being able to better enjoy the dividends of the digital economy, forming a stronger ecological advantage and promoting efficient interaction between systems. For non-resource-based cities, the government supports a diversified economy and the ecological pressure is relatively small. Therefore, the positive effect of government support on the coupling is more significant than that of resource-based cities.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eEconomic foundation\u003c/em\u003e\u003c/strong\u003e. The overall regression coefficient of economic basis for coupling is positive, with the P-value less than 0.01, significant at the 1% significance level. Regardless of geographic location and resource type, it passed the significance test. Specifically, the coefficient in the west is the highest, which shows that for relatively backward regions to a certain extent, the technological progress brought by the new economic form of the digital economy has a stronger enabling effect.\u0026nbsp;Consequently, this situation is more favorable for the This situation is more favorable for of green total factor productivity. The digital economy in the east has developed earlier and has a higher level, which has enabled the release of the digital economy dividends to be more sufficient, and can make better use of the development of the digital economy to transform the traditional high-polluting production model. The economy of resource-based cities is mostly dominated by the mining and processing industries of natural resources such as coal and oil, and the high-tech industry is still in its initial stage. This makes the adjustment of the industrial structure rigid, coupled with the problems of high resource development intensity and low utilization efficiency, resulting in a more prominent contradiction between economic development and ecology. Therefore, the promotion effect of such cities is relatively limited compared to non-resource-based cities.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eSpatial quality\u003c/em\u003e\u003c/strong\u003e. The overall regression coefficient of spatial quality for coupling is positive, with the P-value less than 0.05, significant at the 5% significance level. Compared with other influencing factors, the positive effect is weaker, which indicates that the current driving effect of space quality on the digital economy and ecological efficiency still needs to be improved. The reason is that both ecological protection and digital economy involve a large amount of infrastructure construction, such as solar power stations, high-speed railways, industrial Internet, etc. As a developing country, China\u0026apos;s infrastructure construction still needs to be further expanded and improved. From the perspective of geographical location, except for the east, other locations are not significant, which is mainly due to the superior spatial quality of the east, which greatly promotes the synergy of the digital economy and ecological efficiency. From the perspective of resource types, the spatial quality of non-resource-based cities has a significant positive effect on coupling. For the specific reason, compared with resource-based cities, non-resource-based cities are more dominated by the tertiary industry, have superior spatial environment, and have a strong ability to gather talents, capital and other elements, thereby effectively improving the ability of the digital economy to maintain ecosystems.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn view of the current lack of research on the coupling of digital economy and ecological efficiency and its internal mechanism, more attention is paid to the positive significance of digital economy for the improvement of ecological efficiency and environmental quality (Sturgeon, 2019; Langet al., 2020; Shahbaz et al., \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). In terms of research objects, most of them are targeted at the national and other macro-regional levels, and there are few studies on the prefecture-level city scale. For example, Ulucak and Dankhan (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) explored the relationship between digital technology and carbon emissions in the BRIC countries from 1990 to 2015. Hig\u0026oacute;n et al. (\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) studied the relationship between digital economy and ecological environment in 142 countries. Therefore, based on relevant studies (Belitski et al., 2022; Xu et al., \u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), we carried out research on the interaction mechanism of the two systems at the prefecture-level city scale in China.\u003c/p\u003e \u003cp\u003eFirst of all, we choose the research method scientifically and reasonably. To measure the digital economy system, we compared and drew on the Network Readiness Index (NRI) released by the World Economic Forum (WEF) since 2002, the detailed index of the development of the European Union's digital economy since 2014, and a series of internationally comparable indicators selected by the OECD. Combining the definition of the digital economy (Kim, 2017) with the actual development in China, we choose the currently generally recognized method, and mainly draw on the research of Zhao et al. (\u003cspan citationid=\"CR86\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) to obtain the comprehensive development index of the digital economy. For the measurement of the ecological efficiency system, drawing on the research of F\u0026auml;re et al. (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2001\u003c/span\u003e), we use the green total factor productivity measurement, which has also been recognized by many scholars (Shah et al., \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Zhang et al., \u003cspan citationid=\"CR84\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Han and Chen, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). In terms of the selection of influencing factors, we draw lessons from relevant literature (Cai and Wang, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Zhang et al., \u003cspan citationid=\"CR84\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Williams, 2022; Kostakis et al., 2017), and screen them in the process of empirical evidence. In addition, for the measurement of the interaction between the two systems, we choose the commonly used coupling coordination model.\u003c/p\u003e \u003cp\u003eSecond, the study concludes that the interaction between digital economy and ecological efficiency in prefecture-level cities is enhanced in China, and there are obvious characteristics of spatial and temporal heterogeneity. This confirms that the digital economy has the characteristics of openness, time-space intersection and economic sharing, and has a significant spatial spillover effect on the ecological environment (Lange et al., \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Anagnostopoulos et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). The result is a confirmation and extension of previous research. Although there are few studies that combine the two systems, that is, the heterogeneity of the interaction of the digital economy and ecological efficiency, the combination of multiple literatures can still provide clues for our findings. On the one hand, for the single-system research, An and Yang (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) pointed out the new development brought about by the internet reshaping the geographical pattern of China; Guo et al. (\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) emphasized the spatial-temporal characteristics of provincial digital economy in China; Seferlis et al. (\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) stressed that traditional energy consumption and pollution emissions are posing serious challenges; Zhang et al. (\u003cspan citationid=\"CR84\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) analyzed the spatial-temporal heterogeneous characteristics of ecological efficiency, simultaneously its enhancement mechanism was explored. Combined with the spatial-temporal laws of each system, it can be speculated from the side that there must be heterogeneity in the interaction of the digital economy and ecological efficiency. On the other hand, similar studies can also corroborate our findings. Metcalfe's law and Moore's law of the digital economy determine whether the digital economy can provide intelligent, networked and digital technical support for the green transformation of the economy (Myovella et al., 2013; Truby, \u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Sturgeon (2019) stressed that the digital economy can actively innovate new ecosystems in industry. Asongu et al. (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) found that information and communication technology in Africa has an impact on carbon emissions. Jacob (\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) found that digital technology can contribute to the construction of low-carbon cities. Sareen and Haarstad (\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) found that digitalization plays an important role in environmental improvement and innovative development. Zhou and Yang (\u003cspan citationid=\"CR90\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) confirmed that the coupling of national economic development and ecological environment is on the rise in general, but it is still at a low level, and there are uneven spatial distribution characteristics in various regions. The research of Zheng et al. (\u003cspan citationid=\"CR87\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) pointed out that China's provincial green economy and digital economy are on the rise together, and there is a spatial imbalance pattern of high in the east and low in the central and western regions. He et al. (\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) confirmed that with the development of the digital economy, there is also an upward trend in ecological efficiency, and pointed out that the eastern region has advantages over the central and western regions. Liu et al. (\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) also pointed out that under the background of digital finance, the synergy between economic development and the ecological environment has been continuously enhanced in China, but there are spatial imbalance characteristics. Liang et al. (\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) also revealed that due to the spatial spillover and network effects of the digital economy, eastern China can drive the development and interaction of the central and western regions. Although these studies do not explicitly study the interaction between the digital economy and ecological efficiency, it is emphasized that digital economy can effectively improve green total factor productivity and is expected to meet the future challenges of economic growth. In addition, in China, with the continuous promotion of regional integration, more and more research can provide strong support in many aspects for the strengthening and heterogeneity of the interaction between the two systems.\u003c/p\u003e \u003cp\u003eFinally, we found that each influencing factor has different effects on the system coupling, and there is also heterogeneity in the degree of effect on different geographical locations and resource types. In fact, in addition to the listed factors, there are also abundant researches about influencing factors of the coupling between economic development and the ecological environment (Erdmann and Hilty, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Shaikh et al., \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2020\u003c/span\u003e;Umar et al., \u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e2020\u003c/span\u003e༛Zhou et al., \u003cspan citationid=\"CR89\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). We draw on relevant research and take into account the characteristics of the research system. FDI, population size, and other factors were put into the model for regression. Combining the influencing factors and regression results of the digital economy, through screening and comparison, we finally selected influencing factors such as industrial collaboration, technological innovation, environmental regulation, industrial upgrading, government support, economic foundation and spatial quality. At present, few scholars have analyzed the influencing factors of the coupling of digital economy and ecological efficiency, which highlights the value of our research. From the perspective of relevant research, for technological innovation, most scholars have pointed out the positive role of innovation and the digital economy (Shaikh et al., \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Wu and Yan, \u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). While promoting the development of digital transformation, technological innovation is conducive to the efficient distribution of energy (Murshed et al., \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) and the healthy and stable energy industry (Litvinenko, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). It has an important role and has been confirmed (Xu et al., \u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Scholars also pointed out the reasons for the weak interaction between economy and ecology of the resource-based cities, which was from the viewpoint of technical heterogeneity. However, the positive effect on the coupling of this study is not prominent. It can be seen that China's technological innovation has not fully produced real results. And the development of digital technology in China has not achieved an effective replacement of energy demand. There is an urgent need to unleash the potential of innovation and promote the synchronous leap of the system. In terms of environmental regulation, the government can accurately quantify the pollutant discharge and pollution control capacity of producers through the digital supervision platform, and effectively achieve joint management across departments. Shahbaz et al. (\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) stressed the positive impact of government regulation on renewable energy. Nizam et al. (\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) supported the digital economy to continuously alleviate global pollution by strengthening environmental resource management. Therefore, environmental regulation should have a positive impact on ecological efficiency (Cui et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). However, this study found that it has a negative effect on the coupling, which shows that in the interaction between the digital economy and the ecological efficiency system, it is urgent to strengthen the accuracy of environmental regulation and fully tap its positive effect. Regarding the industrial collaboration factor, Ali et al. (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) emphasized the positive role of industrial synergy in energy internet, digital economy and ecological efficiency. Cai and Wang (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) pointed out that industrial synergistic agglomeration will stimulate the digital economy. Moreover, it will also ensure the cleaning function and promote the improvement of ecological efficiency. For industrial upgrading, the linkage effect, spillover effect and diffusion effect of the digital economy drive the upgrading of the industrial structure. The optimization of the industrial structure will help the entire industrial system gradually evolve to green production (Seferlis and Varbanov, 2014; Shahbaz et al., \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). This will further promote the development of digital economy and green economy (Vassileva et al., 2012; Heo and Lee, 2019). Ren and Du (\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) emphasized that the transformation of industrial structure will effectively improve ecological efficiency, while further releasing and promoting the development of the digital economy. For space quality aspects, Superior spatial quality will compress the space-time distance and increase the flow of factors to stimulate economic growth (Czuryk, 2021). In the digital era, poor spatial connectivity will inhibit economic development (Bowen and Morris, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Philip and Williams, \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Amuso et al. (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) also pointed out that the improvement of space quality can help the digital network effectively integrate resources and reduce carbon emissions. Fan and Xu (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) also pointed out that the government should lay stress on the construction of information infrastructure in the central and western regions, provide financial and equipment support, and strive to bridge the digital divide. In terms of economic foundation, Falk and Hagsten (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) stressed that differences in regional economic development will form a digital divide. the economic strength of each city is the basic condition of the digital economy and the key to improving ecological efficiency. Therefore, the positive effect of this factor on the coupling is undeniable.\u003c/p\u003e \u003cp\u003eIt is worth mentioning that, the study also has certain limitations. On an industry scale, it can be subsequently probed on the basis of three- or four-digit industry to replace the more macro two-digit industry. At the same time, the correlation among the subdivided industries can be further analyzed. In terms of influencing factors, the joint prevention and control of system, culture, environmental regulation, and economic globalization will all implicate the development trend of digital economy and ecological environmental protection. However, due to the difficulty of quantification and the limitation of data acquisition, this study did not conduct in-depth research, but this is also the direction that will be refined in the future. More, we will further optimize the research model in the future.\u003c/p\u003e"},{"header":"Conclusions And Suggestions","content":"\u003ch3\u003eConclusions\u003c/h3\u003e\n\u003cp\u003eThis study explores the heterogeneous characteristics of the interaction between digital economy and ecological efficiency in prefecture-level cities in China from 2011 to 2020, and analyzes its internal mechanism. This has reference value for the precise implementation of policies to promote the systematic coordination of Chinese cities, and is also conducive to achieving a win-win situation for China\u0026apos;s economy and environment. The specific conclusions are as follows.\u003c/p\u003e\n\u003cp\u003eFrom 2011 to 2020, the coupling coordination degree of China\u0026apos;s digital economy and ecological efficiency showed a linear upward trend as a whole, from a transitional state to a coordinated development. In terms of space, there is an obvious jump, and most cities tend to be synergistic in the later period. The absolute difference among cities fluctuated upwards, the relative difference was relatively stable, and the regional linkage was not good. Among them, the coupling of Guangzhou, Hangzhou and Xiamen is the best. This shows that there is a certain spatial-temporal heterogeneity. While there is a positive trend, more balanced interactions are urgently needed. In terms of geographical location, the coupling in the east of China has an obvious advantage, but the growth rate in the west is the most significant. Therefore, regional coordination needs to be promoted, and it is urgent to break down the regional barriers and promote the flow of factors. In terms of resource types, non-resource-based cities are more inclined to diversified development, the coupling of digital economy and ecological efficiency is obviously better than that of resource-based cities, and the interaction is obviously active. This further confirms the urgent need to seek more refined and efficient interactions for different cities according to local and category-specific conditions.\u003c/p\u003e\n\u003cp\u003eIndustry synergy, industrial upgrading, government support, economic foundation, and spatial quality all significantly have a positive effect on the coupling of digital economy and ecological efficiency, especially government support. However, technological innovation has not shown its due positive effect, showing a certain lag. It can be seen that China urgently needs to break through the boundary effect of innovation, strengthen independent research and development, make up for technical defects, and strengthen the compatibility of interaction with the two systems. The negative effect of environmental regulation reflects that the science and precision of regulation need to be improved. The positive role of government regulation and spatial quality in eastern and non-resource-based cities is more significant, which is a reflection of the effectiveness of their development strategies. The central region is actively accepting the industrial transfer from the east, so industrial upgrading has a relatively significant role in promoting coupling. The industrial synergy and economic foundation in the west have a significant positive effect, reflecting the short-term dividends brought by Western Rise Strategy. Furthermore, the positive effect of industrial upgrading on the coupling of resource-based cities that emphasize transformation is also relatively dominant. However, the intensity of environmental regulation in such cities still needs to be scientifically controlled, and the quality of space needs to be improved urgently.\u003c/p\u003e\n\u003ch3\u003eSuggestions\u003c/h3\u003e\n\u003cp\u003eFirst, formulate differentiated urban development policies and strengthen regional linkage effects. Governments should gradually implement digital economy and ecological development policies that are compatible with their local resource advantages. This requires the government to carry out reasonable macro-control and give full play to the effective role of fiscal intervention. For example, the east should promote the continued development of the digital economy to maintain its advantages in improving ecological efficiency. The central and western regions should receive preferential policy resources, seize the opportunities of the digital economy, and increase investment in green technology innovation and digital infrastructure. In addition, accurately grasp the development characteristics of resource-based cities, give full play to the enabling effect of the digital economy on traditional industries, and release the driving potential of technological innovation and spatial quality to the interaction.\u003c/p\u003e\n\u003cp\u003eSecond, release the positive effects of factors such as technological innovation, environmental regulation, industrial optimization, and space quality. While continuing to improve the construction of digital infrastructure, it is also necessary to improve the talent introduction system, strengthen the investment of science and technology in the field of environmental protection, and promote the simultaneous development of production, education and research. To achieve green transformation by transforming traditional industries, promoting emerging industries, promoting the driving force of some digital technology in industrial upgrading, then releasing the development of digital industries. In addition, to promote the organic integration of fiscal policies, administrative orders and market mechanisms, cities should make good use of environmental regulation tools according to actual conditions, strengthen joint prevention and control, and positively adjust the synergy between economy and ecology.\u003c/p\u003e\n\u003cp\u003eThird, promote the deep integration of the digital economy and the ecological efficiency system, and seek synergistic interaction points. In the process of production and consumption, it is necessary to use digital technology to enrich the types of green consumption products, and use more high-quality digital smart products to satisfy and guide green consumption. In addition, the government should actively take the lead in promoting the digitalization of the industry. By strengthening the integration of front-end and back-end industries and the cooperation of upstream and downstream enterprises, the resource consumption on the production end and the pollution discharge on the consumer end should be reduced, thus the coordination of the two systems should be continuously promoted.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAuthor contribution\u0026nbsp;\u003c/strong\u003eZiyan Zheng: conceptualization, methodology, writing\u0026mdash;original draft. Yingming Zhu: reviewing and supervision. Yi Wang: conceptualization, writing\u0026mdash;reviewing, editing. Yaru Yang \u0026amp; Zijun Zhang:\u0026nbsp;data curation.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e This work was supported by the National Social Science Foundation of China [20BJL106], the National Natural Science Foundation of China [41901205]; Cultural Experts and Four Batches Talents Independently Selected Topic Project [ZXGZ[2018]86], and Postgraduate Research \u0026amp; Practice Innovation Program of Jiangsu Province [KYCX21_0357].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthical approval\u003c/strong\u003e This research project has been approved by the Ethics Committee of Nanjing University of Science and Technology.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to participate\u003c/strong\u003e Written informed consent for publication was obtained from all the authors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e The authors confirm that the article described has not been published before; not considering publishing elsewhere; its publication has been approved by all the co-authors; Its publication has been approved (acquiesced or publicly approved) by the responsible authority of the institution where it works. The author agrees to publish in the following journals, and agrees to publish articles in the corresponding English journals of Environmental Science and Pollution Research. If the article is accepted for publication, the copyright of English articles will be transferred to Environmental Science and Pollution Research. The author declares that his contribution is original and that he has full rights to receive this grant. The author requests and assumes responsibility for publishing this material on behalf of any and all the co-authors. Copyright transfer covers the exclusive right to copy and distribute articles, including printed matter, translation, photo reproduction, microform, electronic form (offline, online), or any other reproduction of similar nature.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u0026nbsp;\u003c/strong\u003eThe authors declare no competing interests.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003e\u003cspan\u003eAn T, Yang C (2020) How the Internet Is Reshaping China\u0026apos;s Economic Geography: Micro Mechanism and Macro Effects. Econ Res J 55(2):4\u0026ndash;19\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003eAli MA, Hoque MR, Alam K (2018) An empirical investigation of the relationship between e-government development and the digital economy: the case of Asian countries. 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It is the catalyst for sustainable economic growth and high-quality development in China. Specifically, the study applies modified E-G index, super-efficiency slacks-based measure (SBM) with Malmquist-Luenberger (ML) index, entropy weight Topsis, coupling coordination degree and other models to explore the spatial-temporal heterogeneity of the coupling between digital economy and ecological efficiency. In addition, the internal mechanism of coupling is analyzed from the dimensions of industrial collaboration, technological innovation, environmental regulation, and other aspects. The results show that the coupling between digital economy and ecological efficiency is an upward trend from imbalance to synergy in China on the whole. The distribution of the coupling at the synergistic level expanded from point-like to band-like, and the pattern of spreading from east to the center and west was significant. The number of cities in the transitional level decreased significantly. It can be seen that the jump phenomenon and linkage effect of coupling in space and time are significant. Additionally, the absolute difference among cities has expanded. Although the coupling in the west has the fastest growth rate, the coupling of the east and resource-based cities still has obvious advantages. Therefore, the interaction of systems has not reached the ideal coordinated state, and a benign interaction pattern has yet to be formed. Industrial synergy, industrial upgrading, government support, economic foundation, and spatial quality all show positive effect on promoting the coupling of digital economy and ecological efficiency; technological innovation reflects a certain lag; environmental regulation that has not been fully exerted needs to be used scientifically and accurately. Among them, the positive effects of government support and spatial quality performed better in the east and non-resource-based cities. Because of the continuous optimization of the industrial level, the coupling between the west and resource-based cities has achieved better dividends, but the spatial quality needs to be further improved. Therefore, the efficient coordination of China's digital economy and ecological efficiency urgently needs scientific, reasonable, localized, and distinctive manner.\u003c/p\u003e","manuscriptTitle":"Spatial-temporal heterogeneity of the coupling between digital economy and ecological efficiency and its influencing factors in China","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2023-02-09 20:46:44","doi":"10.21203/rs.3.rs-2476754/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Major Revision","date":"2023-03-17T15:21:01+00:00","index":"","fulltext":""},{"type":"reviewerAgreed","content":"","date":"2023-02-08T22:20:14+00:00","index":0,"fulltext":""},{"type":"reviewersInvited","content":"","date":"2023-02-08T10:54:59+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"Environmental Science and Pollution Research","date":"2023-02-02T16:58:32+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2023-01-18T05:13:06+00:00","index":"","fulltext":""},{"type":"submitted","content":"Environmental Science and Pollution Research","date":"2023-01-13T19:41:34+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"environmental-science-and-pollution-research","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"espr","sideBox":"Learn more about [Environmental Science and Pollution Research](https://www.springer.com/journal/11356)","snPcode":"11356","submissionUrl":"https://submission.nature.com/new-submission/11356/3","title":"Environmental Science and Pollution Research","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"26b128ee-4e9f-47a1-b75b-ed7762cabc9f","owner":[],"postedDate":"February 9th, 2023","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2023-10-16T21:47:57+00:00","versionOfRecord":{"articleIdentity":"rs-2476754","link":"https://doi.org/10.1007/s11356-023-28155-y","journal":{"identity":"environmental-science-and-pollution-research","isVorOnly":false,"title":"Environmental Science and Pollution Research"},"publishedOn":"2023-06-16 21:14:08","publishedOnDateReadable":"June 16th, 2023"},"versionCreatedAt":"2023-02-09 20:46:44","video":"","vorDoi":"10.1007/s11356-023-28155-y","vorDoiUrl":"https://doi.org/10.1007/s11356-023-28155-y","workflowStages":[]},"version":"v1","identity":"rs-2476754","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-2476754","identity":"rs-2476754","version":["v1"]},"buildId":"WrCJVZZCHTDjtuVLN7oU0","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}