Outdoor physical activities can promote public health and they are largely influenced by the built environment in different urban settings. Understanding the association between outdoor physical activities and the built environment is important for promoting a high quality of life. Existing studies typically focus on one type of outdoor activity using interview-based small samples and are often lack of systematic understanding of the activities' intensity and frequency. In this study, we intend to gain deeper insight into how the built environment influences physical activities using the data extracted from individual's wearables and other open data sources for integrated analysis. Multi-linear regression with logarithm transformation is applied to perform the analysis using the data from Changsha, China. We found that built environment impacts on outdoor physical activities in Changsha are not always consistent with similar studies' results in other cities. The most effective measures to promote outdoor physical activities are the provision of good arterial and secondary road networks, community parks, among others in Changsha. The results shed light on future urban planning practices in terms of promoting public health. Recent studies have shown that outdoor physical activities can improve people's physical and mental health by preventing chronic diseases, relieving anxiety, reducing depression, and other mental diseases rates . The urbRegarding the research methods for urban built environment impacts on physical activities, some of the studies focus on the control of physical factors to analyze impacts . FurtherTill now the commonly identified urban built environment factors' impacts on a combination of no-equipment outdoor physical activities which are generally applied in China  , 10 is rFurthermore, at present, most of the research is based on the research participants' home address, school, and workplace to establish a certain buffer for analysis, but less on the participant's motion trajectory –20. The Open data practices such as open street maps (OSM) and satellite images offer opportunities to combine different urban scales with human trajectories. As such, the use of newly available data sources like trajectory data and open data to generate insights with fewer costs becomes feasible, using automatic scripts and tools from multiple platforms.To summarize, the gap that we want to address in the current research is:There is no systematic analysis of one city's urban built environment impacts on outdoor activities from different angles with the help of trajectory data and other open data sources.Therefore, the research question raised in this study is:How most relevant urban built environment elements impact public outdoor walking and running activities, using data from wearables and other datasets, for a city like Changsha.The following part of this article is structured as follows: Background explains in more detail the literature review in relation to the case study area. Methods illustrates the methodology followed. Results and discussion explains the urban planning recommendations based on the analysis results. The Conclusion concludes the research.Regarding impacting factors for outdoor activities, the current research can be divided into three categories. First, built environment factor impacts on physical activities are studied separately, such as the impacts of land-use mix , 25–28, As shown, the existing studies are mainly focusing on one specific city and each city has its own characteristics and therefore, it is important to first understand the context so that we can identify the most relevant built environment factors for Changsha, in combination with the identified factors from literature.Changsha is a city located in the Hunan province of the People's Republic of China. As one of the most urbanized cities in China, the aging population has urged the urban planners and decision-makers to provide facilities to support public health activities like outdoor walking and running activities. In China, the government applies 10 min walking distance as a general guideline for residential area planning. It means that within such buffers, it is desired that the public facilities can support outdoor activities. Therefore, in this study, we have applied an 800 m range to create grids for sampling and further analysis.Changsha has also many universities which form an important destination for many people and therefore, in this research, we have specifically included accessibility to campus as an urban built environment factor to analyze.Based on the existing research results, the most identified factors which influence the urban walking and running activities in Changsha China are given in Research has shown that there is a positive correlation between the degree of mixed land use (LM) and the possibility of people engaging in active physical activity , 56. SpeThe density of urban roads also has an impact on physical activities. As the carrier of residents' physical activities, urban roads are also channels connecting the functions and spaces of various parts of the city. The higher the connectivity in the neighborhood, the shorter the distance for residents to reach various target spaces, which is more conducive to increase relevant traffic physical activities, such as leisure physical activities like walking and running , 43. ResPublic facilities' density is also one of the main factors affecting the frequency and intensity of physical activities. A study found that more parks can effectively increase users' walking frequency and each additional piece of the facility will increase the probability of users' physical activity by 12% . In ChinGreenery, in general, has a general impact on outdoor physical activities. Areas with a high normalized vegetation index are more conducive to physical activity , 67. To As mentioned in the introduction part, there is less research on combining the different angles of outdoor walking and running activities like frequency and intensity into one research, while it is important to combine these angles to fully understand such physical activities and design customized urban areas to promote these two activities accordingly. Therefore, this study has selected frequency and intensity as the dependent variable for walking and running activities .In this section, the data collection and processing approaches are explained (Data collection and processing) together with the analysis models used (Multiple linear regression with logarithmic transformation).https://glovis.usgs.gov/). Landsat 8 TM image data are imported into ENVI, and radiometric calibration on the original data is performed. The calibrated data image is used to perform the atmospheric correction. After completing the radiometric calibration and atmospheric correction, NDVI calculation on the corrected TM image data is performed by the vegetation NDVI tool in the spectrum processing toolbar.The built environment factors' data in the research area come from multiple sources, which mainly include urban land use type data, road network data, building vector data, urban point of interest (POI) data, and NDVI data. The urban land use type classification data comes from the master plan maps, which is vector data and is adjusted according to the most updated version. The road network data is processed and classified based on the data obtained from the OSM and the not completed parts are supplemented by the traffic status maps. The vector data of buildings is obtained through Gaode Map's open API  and caliThe outdoor walking and running physical activities' data and the trajectory data are obtained from smartwatch providers (Duorui wearable and application) and all the personal data have been deleted to protect the privacy and the users have all signed agreements to allow data gathering for research purposes. The research received a waiver of approval from the ethics committee, since it is not involving identifiable human subjects. The consent was given for data collection by all participants. To normalize the datasets and generate samples from the data obtained, the ArcGIS fishing net tool has been used to generate vector points in the study area. According to the actual situation of the urban built environment and the trajectory distribution in the study area, the sample points that fall on the water, non-built-up areas, and the surroundings are deleted. After this process, the sample size is obtained for further model development and analysis.The dependent variables are calculated as follows: the frequency of physical activity is calculated by the number of trajectories in the sample using ArcMAP spatial connection tool. The intensity of walking and running activities is calculated by the total length of trajectory in the samples using ArcMAP clipping and spatial connection tools. With the help of ArcMAP intersection tool, the trajectory data within the research areas are screened out and the data are cleaned by deleting short motion time, short motion distance, and abnormal motion trajectories.The independent variables in the models are calculated as follows:Land-use mix refers to the degree of diversification of urban land within a certain range. The entropy index model is used to determine the degree of land-use mixing.where Piis the proportion of the ithtype of land use in the current grid area, and N is the number of land use. The calculated land-use mixing degree value is a number from 0 to 1.Residential land density refers to the ratio of the area of residential land within a certain range to the total area of the range.where RLA is the area of residential land in the area (m2) and AREA is the total area (m2).Building density refers to the ratio of the area of the building base (BA) within a certain range to the total area of the range, reflecting the density of buildings.where BA is the area of the building base (m2) and AREA is the total area (m2).Arterial road density refers to the ratio of the total length of the arterial road (ARL) within a certain range to the total area of the area.where ARL is the total length of urban arterial roads in the area (m), and AREA is the total area (m2).Secondary road density refers to the ratio of the total length of the secondary road (SRL) within a certain range to the total area of the scope.where SRL is the total length of secondary arterial roads in the region (m) and AREA is the total area (m2).Branch road density refers to the proportion of the total length of branch roads in a certain range to the total area of the range.where BRL is the total length of urban branch roads in the area (m) and AREA is the total area (m2).University campuses density refers to the proportion of college campuses within a certain range to the total area.where UCA is the area of college campuses in the region (m2) and AREA is the total area of the region (m2).Comprehensive parks density , and AREA is the total area (m2).Normalized difference vegetation index refers to the ratio of the average value of NDVI raster data pixel values to the number of pixels within a certain range, reflecting the vegetation coverage within a certain range.where NDVIiis the DNVI value of a single pixel in the area, and N is the number of pixels in the area.Community park density, bus stop density, square density, market density and living service points density refer to the number of facilities in a certain range. It is calculated based on POI data since these are all small facilities which make it not possible to calculate the area size. Therefore, nuclear density analysis in ArcGIS is first applied to generate a grid, and then the sample grid created is used to extract its grid density value.Multiple linear regression (MLR) is a statistical technique that employs several explanatory variables to predict the outcome of a dependent variable. The objective of MLR is to model the linear relationship between the explanatory variables and a dependent variable . More thwhere y denotes the dependent variable of observation, xndenotes the nTH explanatory variable for the ith0 is the intercept (constant), βn is the slope coefficient for the nth explanatory variable, and ε is the error term observation, β.Multiple linear regression is a function that allows us to make predictions about dependent variables based on the information that is known about several independent variables. When applying MLR, there is a hypothetical linear relationship between the dependent variables and independent variables. The independent variables should not be highly correlated with each other. The observations of dependent variables must be selected independently and randomly from the sampled population. In addition, the residuals should be normally distributed with a mean of zero and variance σ.R2 is used as a metric to measure how much the variation in the dependent variable can be statistically explained by the variation in the independent variables. The value of R2 is between 0 and 1, where 0 means the outcome of the dependent variable cannot be predicted by any of the independent variables, and 1 indicates the outcome of the dependent variable can be perfectly predicted by the independent variables without any errors. In estimation, the slope coefficients are valid while holding all the other variables constant. The R2 is monotonous increasing with the number of variables, which leads to the alternative of adjusted R2 . MLR models with logarithmic transformation are generated using the sample trajectories (Result of MLR-LT). The following part of this section illustrates the analysis results and the indications on urban built environment planning practice (Discussion).Data on walking and running activities were collected in Changsha from the year 2016 to the year 2018. Based on the 35,019 effective samples of trajectories, we can not only obtain the information of physical activity types but also other information such as spatial locations, starting time, duration, and distance. In particular, 10,773 effective walking data and 5,879 effective running data are obtained, as shown in The data used for characterizing the built environment are getting from the master plan maps of Changsha (2003–2020), OSM and the traffic status maps of Changsha (2017–2035), Gaode Map API , and LanSome differences in the spatial and temporal distribution have been found between walking and running. From the perspective of time distribution, people mostly go on physical activities in the morning (5:00–8:59) and evening (18:00–20:59), and the average activity time of walking is longer than that of running, while the average activity distance and average activity speed are lower than that of running. From the perspective of spatial distribution, walking and running activities are distributed in the areas with relatively good urban construction conditions, but the scope of walking is larger than that of running. Compared with walking activities, running activities are less concentrated shown in .Vector points with a distance of 800 m in the study area are generated to normalize the datasets. After deleting sample points that fall on the water, non-built-up areas, and the surroundings, there are 735 samples left for further analysis .R2 of each MLR-LT is shown in R2 and R2 and R2, the significant coefficients still represent the mean-variance in the dependent variables for one unit of change in the independent variable while holding other independent variables in the model constant.The The estimated results regarding the frequency of walking and running are shown in The walking and running frequency are both significantly influenced by the residential land density (RLD), community parks density (PD), university campus density (UCD), square density (SD), arterial road density (ARD) and secondary road density (SRD) in a positive way. It is easy to understand the effect of RLD since the location of the home is normally the start point of outdoor activities. The spaces and facilities in the park, university campus, and square are conducive to physical activities. The arterial and secondary roads are also providing routes for walking and running, especially in dense urban environments.The land use mix (LM), normalized difference vegetation index (NDVI), branch road density (BRD), bus stop density (BSD) and community park density (PD) are proportional to the frequency of walking, whereas their effects on running frequency are not significant. This finding distinguishes the different needs of spatial settings and infrastructures across walking and running activities. The land-use mix level has significant positive impacts on walking frequency, which could be explained by the multiple intentions of people for activities, like grocery shopping, meeting friends, visiting green spaces, etc. The increase of NDVI results in more greenery coverages, which to some extent attracts light outdoor activities, resting, walking, among others. The activity that happens close to vegetation, to some extent, implies people's preference for greenery when walking in outdoor urban environments. However, NDVI is not significantly related to the frequency of running. In real-world contexts of the study locations, the mixed-use branch roads with sidewalks are leading to the nearby neighborhoods, shops, restaurants, and other destinations. It is reasonable to assume that, during walking, residents may carry out other activities simultaneously as above mentioned. The BSD and PD are increased in the areas of more residential neighborhoods. Therefore, the positive effect of the BRDs, BSD, and PD on walking makes sense. On the other hand, the runners must start and end the running in their residential neighborhoods where many branch roads, bus stops, and community parks are located, but they may avoid the branch roads, bus stops even community parks in other neighborhoods, since they are not the right places for running.The living service points density (LSD) only significantly impacts the frequency of running. The increase of LSD results in less frequency of running. For the sake of efficiency and running conditions, the runners are inclined to avoid the areas with living service points on purpose. Thus, less frequent running trajectories come out in a place with a high LSD.Concerning the intensity of walking and running activities, the results of the estimation are shown in Land-use mix (LM), branch road density (BRD) and bus stop density (BSD) are proportion to the walking intensity, whereas they are not significantly related to the intensity of running. Compared with running, walking is more relaxed and less constrained by the tempo-spatial settings. In some cases, the walking activity is combined with leisure, shopping, social activities in pedestrian business areas, where many brand roads and bus stops are located nearby.The living service points density (LSD) negatively influences running intensity which is similar to its effect on running frequency. The market density (MD) has no direct significant impact on both walking and running intensities.Multicollinearity will decrease the statistical significance of the regression model. As the diagnosis for multicollinearity, the variance inflation factor (VIF) of MLR-LT is given in The increase of land-use mix (LM) will promote the frequency and intensity of walking while there is no correlation found of land use mix with the frequency and intensity of running activity. This is probably because the higher land-use mix means more abundant destinations and urban functions, which will increase the willingness and intensity of residents to carry out walking activities that can help them achieve several goals like grocery shopping, exercising, and picking up packages. For urban planners, it is important to understand the local communities' preference for outdoor activities to design a balanced land-use mix and prevent unnecessary traffic flow to promote walking activities.The residential land density (RLD) has positive impacts on both running and walking activities regarding intensity and frequency. This could be explained by that residents all need to start and come back to their residence. However, we can see that RLD has a higher impact on walking activities than on running activities which is probably because runners favor places with more open areas rather than the high residential building density areas.Interestingly, building density (BD) and market density (MD) have no correlation with both running and walking activities, which is in contrast with the literature.Community parks and squares play also a very important role. The results show that PD and SD have a high positive correlation with the frequency and intensity of walking and running activities, and the positive impacts are higher for running than that for walking. Compared with the other types of leisure places like a comprehensive park, the community parks and squares are smaller in overall area and less perfect in facilities and landscape, but they are normally more accessible and relatively easy to develop, which makes up for the lack of service scope of the comprehensive parks to a certain extent. In contrary to the PD and SD, the CPD has a fewer impact on walking activities and no direct influence on running frequency, but it does have positive impacts on running intensity. It could be explained that comprehensive parks are not that many, and it is not common for people to run that far. Therefore, community parks and squares can effectively promote the intensity of residents' activities in a certain range. So, community parks and squares should be further promoted in densely populated cities urban planning practice.University campus density is with even higher correlations to walking and running activities, compared with community parks and the impacts of UCD on running activities is much higher than that on walking activities. This indicates that the campus space carries an important role in promoting physical activities, especially for running. Potential guidelines to use the open campus as a running activity base could be developed, particularly for cities like Changsha which has many universities.Different types and levels of urban road density affect the frequency and intensity of physical activities on different levels. The urban road factors selected in this paper include arterial road density (ARD), secondary road density “SRD” and branch road density (BRD), and the results show that the urban ARD has a big positive correlation with the frequency and intensity of physical activity, and it has more impacts on walking activities than on running activities. From the regression results, the influence of urban SRD is still positive, but SRD promotes running activities more than promoting walking activities. However, the BRD only has a limited amount of positive impact on walking activities while no direct correlation with running activities. It could be argued that based on the results, context-specific road network designs can be improved. For example, the increase of SRD will significantly promote the frequency and intensity of running activities and the increase of ARD will significantly promote walking activities. For areas with more aging populations, the road network could be with more branch roads to promote walking activities.Living service points density has no correlation with walking activity but a bit negative influence on running activities, which could be explained that the living service points we have referred to here are all facilities running during daytime while the walking activities are mainly happening not during the daytime and running activities are not prone to stops.The normalized difference vegetation index (NDVI) mainly affects walking activities. The results show that walking is more likely to be carried out in the urban environment with greenery, while running may have lower requirements for landscape greening than walking, and it is more likely to choose the path with connectivity and comfort and NDVI has no direct correlation with running frequency but positive impact on running intensity. Therefore, for urban planners, general greenery should be provided to promote walking activities and if combined with parks and squares, it could potentially stimulate running intensities.The highly populated and urbanized cities in China ask for high-quality built environments that can facilitate and stimulate outdoor physical activities to benefit social cohesion and public health. In the literature, we have found quite some factors that can influence outdoor physical activities. However, three main limitations still remain. First, the findings of previous studies are mainly case-based without consideration of environmental conditions, which need to be fully understood while designing a generic approach to generate insights. Furthermore, the previous studies have not analyzed physical activities from angles of frequency and intensity. Third, the size of sample data is limited in the existing literature. This research proposes a conceptual framework with the comprehensive influences of urban built environments for identifying commonly found urban built environment factors that influence people's walking and running frequency and intensity for a case city.This study has, therefore, contributed to: (1) developing a systematic analysis approach for the relationships between the urban built environment factors and outdoor physical activities for a city; (2) identifying effective and significant influential factors based on the city context; (3) presenting the method of collecting and processing data from smart wearables and OSM, Gaode and remote sensing data to analyze outdoor physical activities trajectories and patterns in the urban built environment; (4) addressing the process and results of GIS spatial analysis to obtain physical activities' intensity and frequency and cleaning trajectory data to generate realistic samples; (5) developing multiple models to analyze the impacts of multiple urban built environment factors on two types of outdoor activities, from both frequency and intensity angles; and (6) generating urban planning insights based on the frequency and intensity analysis of the most two favored outdoor activities for the case study region.This research collects data generated from smart wearables and open geographical datasets like OSM, Gaode Maps, and remote sensing images. The approach is feasible for investigations on urban outdoor physical activities in other regions. Moreover, the findings of this study have implications for related urban policymaking in Changsha and other cities in China with similar environmental and socio-cultural contexts. To be more specific, such findings can help decision-makers in their planning activities as a starting point for discussing with citizens in the pursuit of a better urban built environment which helps to promote public health. Possible starting points could be providing more community parks, maintaining or improving road networks, designing open campuses, etc.More angles from physical activities like starting time and duration are expected in the future studies. On one hand, analysis of the relationships between built environment characteristics and other outdoor physical activities, such as cycling, dancing, etc., can be followed using the same approach. In addition, the detailed analysis can be performed regarding the difference between recreational walking and transport walking . On the libo0910@csu.edu.cn.The data analyzed in this study is subject to the following licenses/restrictions: Some data is not publically available, and follows commercial license. Requests to access these datasets should be directed to BL All the personal data have been deleted to protect the privacy and the users have all signed agreements to allow data gathering for research purposes. The research received a waiver of approval since it is not involving identifiable human subjects. Written informed consent was given for data collection by all participants.BL, QL, and HH: conceptualization and methodology. QL: software, data curation, and visualization. QL, YP, TF, and TW: formal analysis. BL, HH, and TW: resources. QL and TW: writing—original draft preparation. TW, YP, and TF: writing—review and editing. BL, HH, YP, and TW: supervision. All authors have read and agreed to the published version of the manuscript.The APC is funded by the Delft University of Technology.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


How enterprises should practice digitalization transformation to effectively improve green innovation performance is related to the sustainable development of enterprises and the economy, which is an important issue that needs to be clarified.This research uses the perspective of production and operation to deconstruct the digitalization of industrial listed enterprises from 2016 to 2020 into six features. A variety of machine learning methods are used, including DBSCAN, CART and other algorithms, to specifically explore the complex impact of enterprise digitalization feature configuration on green innovation performance.(1) The more advanced digitalization transformation the enterprises have, the more possibly the high green innovation performance can be achieved. (2) Digitalization innovation is the digitalization element with the strongest influence ability on green innovation performance. (3) As the advancement of digitalization transformation, enterprises should also focus on digitalization innovation input and digitalization operation output, otherwise they should pay attention to digitalization management and digitalization operation output.The conclusions of this research will help enterprises understand their digitalization competitiveness and how to practice digitalization transformation to enhance green innovation performance, and also help the government to formulate policies to promote the development of green innovation in the digital economy era. Since the reform and opening up, the extensive growth model helps China's economy dramatically transform into one of the relatively important economies, but this development model also causes ecological problems like greenhouse gas emissions and excessive resource consumption , thus enIn addition, with the emergence and development of various digitalization technologies such as AI, cloud computing, and Internet of Things, many countries have also paid attention to them and promoted various related policies. For instance, Industry 4.0 proposed by Germany in 2013 emphasized the development of modern industry by intelligence and digitalization. The Chinese government's proposal for Intelligent Manufacturing 2025 also emphasizes the introduction of digitalization technologies into enterprises in the real economy. At the economic level, the importance of the digital economy has also increased. Further taking China as an example, the scale of the digital economy will reach 19.2 trillion yuan in 2021, accounting for 38.6% of the GDP. These external policies and economic environment will inevitably affect the production and operation of enterprises. Existing research has shown that with the understanding and application of digitalization technology, the business model, production operation, and organizational practices of enterprises have undergone major changes . MoreoveAll in all, in the context of digital economy and green innovation becoming more popular, digitalization and green innovation will have an important role on enterprise development and social progress, and the relationship between them needs to be fully clarified. Specifically, how companies should choose an appropriate digitalization transformation path to help improve green innovation performance is worthy of scholars' discussion. This is directly related to the healthy development of enterprises and the sustainable development of the economy, and may even be related to the public health. Therefore, our research aims to address this issue and analyze the hidden impact of digitalization on green innovation, thereby providing theoretical contributions to academia and recommendations for corporate practice.In order to successfully complete the objective of this research and determine the parts of the research, the background is worth reviewing again. In the era of information explosion, the investigation of the research subjects is carried out in a multi-dimensional manner, and the processes of data collection also tend to be diversified, so the investigating method of enterprise digitalization also needs to be based on a multidimensional deconstruction perspective with reasonable logic. In addition, the relationship analysis between research objects should be guided by multi-source heterogeneous data, and should inform the observers what the real functional relationship is based on the inherent information on the data. Therefore, research on how enterprise digitalization affects green innovation performance should be driven by multidimensional data. Specifically, this study may consist of the following three parts: First, in order to investigate and deconstruct enterprise digitalization with a reasonable logic, we use enterprise production and operation as the perspective to deconstruct enterprise digitalization into various digitalization features according to different types. On this basis, we use public data including annual reports to achieve objective and accurate measurement of the above-mentioned digitalization features. Second, the large volume of data makes it necessary to employ techniques such as clustering to extract information and hidden patterns . Within This study has the following two contributions for the understanding of digitalization and green innovation. (1) From the perspective of production operation, enterprise digitalization is deconstructed into various features, and uses multi-source heterogeneous objective data to quantify these features. This deconstruction idea can not only satisfy the research paradigm which is conducted by multi-features, but also aggregate the above features into a digitalization indicator, thus providing a more comprehensive measurement method for digitalization-related research and helping enterprises to understand their own digitalization maturity. (2) We apply a relatively new research paradigm of data-driven analysis (DDA), which may be a complement to current prevailing paradigms. This paradigm uses popular machine learning algorithms including the cluster analysis and the decision tree model to conduct research on the basis of multi-source heterogeneous data, so that it can more fully explore the complex impact of enterprise digitalization on green innovation performance. (3) Based on the conclusions of our research, enterprises can judge their own digitalization capabilities and find suitable digitalization transition ideas for improving green innovation performance, so as to effectively help enterprises to be more innovative and sustainable in the context of a digital economy.The remainder of this paper is organized as follows. In Section 2, Theories, cases, and quantitative studies related to digitalization and green innovation will be presented. In Section 3, research process, deconstruction ideas of enterprise digitalization, and data source and processing are proposed. In Section 4, the analytical methods used in this research are introduced in detail. In Section 5, the results of the data analysis are clearly displayed. In Section 6, we put forward the conclusions and enlightenments, analyze the shortcomings of this research, and figure out the directions of future research.As green, intelligent, and other characteristics have become the development needs and trends of future industry , enterprThe literature review includes four parts: (1) Theoretical analysis—to explore why enterprises choose digitalization transformation and the impact of digitalization on green innovation. (2) Case analysis—analyze the impact of digitalization on green innovation in a real-world situation. (3) Quantitative research - review the current work on quantitative analysis and lay the foundation for this research. (4) Literature review - discuss the above contents and analyze their possible defects.Whether and how an enterprise decides to undergo digitalization transformation depends on its mindset . In the The impact of digitalization on the innovation component of green innovation has been discussed above. And digitalization also has a positive effect on the green part of the green innovation. The advancement of digitalization technology has enabled the dissemination of green concepts such as sustainable development at a faster rate. For example, narratives such as “climate change is a hoax” can spread very quickly due to the networked nature of our social media existence . Given tIn a realistic scenario, there are many specific cases that can reflect the impact of digitalization on green innovation performance. In China, traditional enterprises represented by industrial enterprises are the focus of green innovation. These enterprises can embed digitalization technologies and platforms such as industrial robots and 3D printing into the entire production and service process of traditional manufacturing enterprises according to their own needs, thereby promoting digitalization transformation to improve green innovation performance . Taking Additionally, He et al.  found thSome scholars have also discussed the impact of enterprise digitalization on green innovation from the research process of quantitative analysis, and have drawn relevant conclusions. El-Kassar and Singh  conducteThrough retrospect of theoretical derivation, case analysis, and quantitative research, there are sufficient reasons to believe that it is inevitable for enterprises to take digitalization-related actions, and the relationship between digitalization and green innovation is also possible. Therefore, it is meaningful to carry out research about the impact of enterprise digitalization on green innovation performance. However, there are still some aspects for improvement, which will be realized in this study: (1) Deriving the impact of digitalization on green innovation only from theoretical deductions seems insufficient, which needs to be supplemented by new evidence from the real-world; (2) Although using the method of case analysis can reflect the specific situation of one or more enterprises and has reference significance, it cannot pay attention to the various heterogeneity differences between enterprises, especially between digitalization; (3) How to reasonably construct and accurately measure enterprise digitalization is the basis of carrying out quantitative analyses, but the current research has the problems of diversification of data sources and inability to unify inspection methods, which may hinder the scholars in carrying out digitalization-related research and enterprises to understand their digitalization competitiveness, so it is necessary to propose a more rigorous and comprehensive enterprise digitalization deconstruction logic; and (4) Most of the existing quantitative research was carried out from an integrated perspective, which only explored the simple impact relationship between enterprise digitalization and green innovation. Therefore, it is difficult to analyze the complex impact of digitalization on green innovation and to provide practical advice for enterprises.In order to successfully complete the research objective, this section contains the following content: the general research process, the deconstructing idea and result of enterprise digitalization, the quantification of green innovation performance, and the source and processing aspects of data. These are the foundational work to explore the complex impact of enterprise digitalization on green innovation performance, so an introduction to them is necessary.Objects in the world usually need to use multiple dimensions or features depicted together , and the(1) Deconstructing enterprise digitalization and measuring green innovation performance. Enterprise digitalization should be depicted by using multiple features, which requires a reasonable deconstruction method of enterprise digitalization. By consulting relevant literature, we find that it is logical to take the production and operation of enterprises as a deconstruction perspective. Enterprise digitalization can be decomposed into multiple features in several dimensions on the basis of production and operations. Similarly, the method of green innovation performance also can be identified by reviewing literature. Finally, we obtain the original data of digitalization features and green innovation performance from databases, and remove the outliers by using the Isolation Forest algorithm to obtain the data resources required for the research.(2) Identify different digitalization transformations. In order to carry out specific analysis on specific problems, the digitalization features data are aggregated into certain clusters with the principle of similarity within the group and dissimilarity between the groups. On this basis, each cluster is appropriately named according to the features' distribution, thereby identifying different types of digital transformation. In addition, green innovation performance is also discretized into different grades, and combined with the clustering results of enterprise digitalization features to analyze the impact of different digitalization transformations on green innovation performance.(3) Explore the complex impact of enterprise digitalization on green innovation performance. In order to continue to analyze the complex impact of enterprise digitalization on green innovation performance on the basis of identifying the digitalization data distribution of different enterprises, we take the obtained various digitalization features as conditional attributes, take the grade of green innovation performance as the decision attribute, and use the decision tree algorithm to mine the decision rule. Therefore, we can analyze what digitalization transformation path is conducive to the improvement of green innovation performance, and put forward corresponding conclusions and enlightenments accordingly.At present, the methods of investigating the enterprise digitalization are diversified and have not yet reached a unified state, but there are only two ways: objective and subjective. For example, using text mining methods to analyze the information related to enterprise digitalization from annual reports , by onlyProduction operation can be a reasonable research perspective for deconstructing enterprise digitalization. When discussing digital transformation, Berman  and Van Based on above research, it is believed that production operations are taken as the investigation perspective, and deconstruct enterprise digitalization from the three dimensions of input, output, and management. However, it is still difficult to accurately cover all information only from the above three dimensions. For example, there are various types of input including innovation input or asset input. Therefore, it is necessary to continue to decompose the above dimensions according to different types, and finally obtain the six enterprise digitalization features shown in As shown in Digitalization input (DI): The actual expenditure and investment of the enterprise on digitalization. There are three features in this dimension, namely digitalization asset input (DAI), digitalization operating input (DOI), and digitalization innovation input (DII).Digitalization asset input (DAI): The more an enterprise invests in digitalization assets, the more it can reflect the actual application of digitalization. Existing research found that the response of incumbents to digitalization elements such as technology may differ depending on the resources or assets that need to be mobilized : In the process of analyzing the production operation of enterprises, the operating cost should be given attention. Therefore, when deconstructing the digitalization of enterprises from the perspective of production operation, it is necessary to analyze the operating cost as one of the features of the input side. Digitalization operating input is the consumption and investment of enterprises in daily operating activities because of digitalization-related things. It is similar to digitalization asset input, which can reflect the application of digitalization, and may also affect green innovation through similar mechanisms. The quantification of this feature also needs to be based on digitalization-related keywords, manually pick the digitalization-related items from the operating costs of the financial statements. The above items are then aggregated vertically by enterprise and year, and divided by total assets to enhance comparability between data.Digitalization innovation input (DII): Scholars have identified that the importance of innovation for the enterprises digitalization transformation : The output obtained by the enterprise in the process of production and operation due to digitalization. There are two features belonging to this dimension, namely digitalization operation output (DOO) and digitalization innovation output (DIO).Digitalization operation output (DOO): In the process of analyzing the production operation of enterprises, operation income should also be given attention. Digitalization operation output is the output obtained by the enterprise in the daily business activities due to the digitalization of related things. Therefore, when enterprises attach importance to this feature, they can avoid warehouse backlogs and promote green innovation performance in supply chains and logistics chains. The measurement of this feature is similar to digitalization asset input, which is also necessary to manually pick digitalization-related items from the operating income of financial statements based on digitalization-related keywords. After completing the above steps, we aggregate vertically by enterprise and year, and then divide by total assets to enhance comparability of data.Digitalization innovation output (DIO): Also due to the existence of the “ripple effect,” when investigating the enterprise digitalization, it is also necessary to consider the digitalization innovation output, and analyze its impact on the green innovation performance. The measurement of this feature is relatively simple, which is to select the application number of digitalization-related patents in a certain year as the measurement basis for this feature. The acquisition method of this feature data is to design a python automation program and crawl from the patent database based on the digitalization-related keywords.There are several categories of Chinese patents, including patents for innovation, utility models, and industrial designs, and these patents have different values, so it is necessary to give different weight to them when measuring this feature.DIOi is the digitalization innovation output belonging to the i-th enterprise. Where Digitalization management (DM): There is only one feature with same name in this dimension, which reflects the relationship with digitalization when making management decisions. According to the mechanism of “mindsponge,” enterprises absorb new cultural and ideological values in a multi-dimensional environment, which will help them adapt to the new environment and identify better development opportunities  published by the National Bureau of Statistics in China, establish a digitalization keyword dictionary, including a total of 166 digitalization-related words including big data, cloud computing, and so on. Second, take the annual reports of all enterprises in the same year as the corpus, and use a term frequency inverse document frequency (TF-IDF) algorithm to calculate the digitalization sentiment weights for the above words according to each year. Third, the sentiment weights of the above keywords are added up according to the year and the enterprise, so as to obtain the raw data of digitalization management.tunities , 47. Thetunities , which cGreen innovation performance (GIP): This is the final output of enterprises in green innovation. Academia's focus on green innovation performance mainly focuses on technological innovation output, so this measurement is based on the cumulative realization of the number of green patent authorizations obtained by enterprises within a certain period of time. In addition, the World Intellectual Property Organization (WIPO) stated in IPC Green Inventory that the identification of green patents is based on the IPC classification number, while industrial designs do not own an IPC number, so green patents mainly focus on the invention authorization type and the utility model in our research. The measurement formula of green innovation performance is as follows:GIPi is the green innovation performance belonging to the i-th enterprise. Where The production and operation of an enterprise needs to adjust its expectations for the future in a timely manner according to the current situation, and formulate appropriate goals to implement changes. The topic of this research is what digitalization transformation steps should enterprises take to help improve green innovation performance, which includes the needs of companies looking forward to the future based on the current situation. The analysis of the current situation of the enterprise is usually based on the year-end situation, so the data used to measure the digitalization features are the ending value for the same period. In addition, because green innovation performance is the foothold of this research, the number of green patents authorized are used as the basis of measurement, with a lag of one period to ensure logical consistency.According to the quantitative requirements, the data types used in the research include financial statements, annual reports, and patent data. The financial statement data comes from the China Stock Market & Accounting Research Database (CSMAR) and RESSET database, the annual reports come from CNIFO, and the patent data is jointly acquired from the China National Knowledge Infrastructure (CNKI) and the China Research Data Service Platform (CNRDS). These databases are relatively authoritative and widely used by academia in China. Since the research focuses on green innovation performance, it mostly focuses on real economy enterprises, so the scope of enterprises is locked in industrial enterprises. In addition, the time span selected for our research is from 2016 to 2020, which is the year when the concept of digital economy and green environmental protection received more attention. This period is also the peak period when China launched a combination of policies to promote innovation and sustainability.After the raw data was processed by the above quantification process, it needed to be matched with enterprises and years. In order to ensure the reliability of the research results, the null values were first removed from the original data. Then the isolation forest algorithm also removed abnormal data on the above basis. Combining the above steps, 8,154 objects with matching enterprises and years were finally obtained.As shown in the research ideas of eps  and MinPts (the minimum number of points in this radius), so as to determine the minimum number of samples existing in a certain range to assume a distribution density threshold, and to cluster data points based on the density range. As shown in In the era of digital economy, a large amount of data needs to use clustering algorithms to assist in mining the hidden information. Nowadays, clustering methods have been widely used in time series analysis , bibliomeps belonging to it is greater than MinPts. If the point satisfies this condition, it is considered as a core point and the points within the density range are clustered into a cluster; otherwise the point is regarded as a border point (non-core point) and skipped. Additionally, this algorithm continues to search for the other point until all data is traversed and obtains all clusters. After the research data is processed by this algorithm, it can be effectively distinguished according to the features data distribution, so as to ensure that the analysis results have sufficient pertinence and practical value.The algorithm randomly selects an unprocessed point from research data and judges whether the number of adjacent points within As a decision attribute, green innovation performance (GIP) needs to be classified in advance. Since the research data is numerical data, the SAX algorithm is the algorithm of choice for symbolizing and discretizing the data to objectively obtain a green innovation performance rating. This algorithm was originally used for time series dimensionality reduction , but it There are three main steps in this algorithm. First, the grading work needs to be carried out under unified standards, so the loaded data must be standardized. Second, the standardized data needs to be dimensionally divided into sub-intervals of equal length by using the method of Piecewise Aggregate Approximation (PAA), and replaced by the mean of each sub-interval . Third, As shown in Gini) to reflect the impurity of the feature data. The smaller the Gini, the lower the impurity. It also uses the conditional Gini coefficient Gini(D∣k) to reflect the feature importance and alternative priority of the conditional attribute to the decision attribute. The smaller the coefficient, the higher the importance and alternative priority of the feature. During the operation of the algorithm, the cut value (k) with the smallest Gini(D∣k) is selected to generate a binary tree. After each binary tree is formed, the algorithm also calculates the Gini(D∣k) for the divided parts to continue to generate branches until it can no longer be divided.In order to find the ideas about improving the green innovation performance (GIP) from the digitalization data clusters of different enterprises, and to know what kind of digitalization transformation processes enterprises should implement to help improve the green innovation performance (GIP), the CART decision tree algorithm is used to extract the decision rules and obtain the complex relationship between the former and the latter. As a traditional classification algorithm, the CART decision tree algorithm can effectively identify the optimal feature configuration for a certain state . CompareDn clustered by DBSCAN has K features, the probability that each value k may be taken for these features is pk , the basic calculation formula of Gini:Assuming that a cluster kn is all the values Kn of a feature in Dn, Dn divided by kn, then the conditional Gini coefficient  The Path means the promotion path or decision path, which is also a specific decision rule used to describe the performance of green innovation by feature configuration. (2) Support is expressed as the probability of itemsets appearing in the total itemsets, that is, the proportion of the sample size of the decision path to the total number of cluster samples. If the total number of samples is T and the sample size of a decision path is P, then the support of the path is Support = T÷P× 100%. (3) Confidence represents the probability that a specific event occurs under a certain condition, that is, the ratio of the number of samples corresponding to the final classification of the path to the total sample size of this path. If the final classification of a decision path is high, where the number of high samples is H and the sample size of this path is P, Confidence in the case of high-grade classification is Confidence = H÷P× 100%. (4) Lift represents the ratio of the probability of a specific event occurring under a certain condition to the probability of the event occurring under the global conditions, so Lift of a certain grade can also be regarded as the ratio of the Confidence of this grade to the Confidence of the total sample. If the final classification of the decision path is high, the high-grade's Confidence of this path is C, and the total sample high-grade Confidence is O, then the Lift is Lift = C÷O× 100%. Lift greater than 1 indicates that the decision path has a positive correlation with a certain state, and the higher the Lift, the stronger the positive correlation, and vice versa. For the convenience of presentation, the Lift displayed in our research is described according to the final grade of the decision path; if the grade obtained by the path is high, the calculation of the Lift is carried out according to the same high-grade.In the decision tree, there are four concepts, l et al. , these cPath1. The Confidence of this path is 97.8% and the Support of it is 30.7%, which means that there is sufficient confidence to believe the classification obtained by this path. Further, the Lift of this path is 2.94, which means this decision rule has a strong positive correlation with the classification of Iris Virginica.As shown in After the research data is processed by the CART decision tree algorithm, several important features and a classification path combined with these features can be obtained. Based on these rules, the nonlinear combination effects of different digitalization features on the green innovation performance can also be analyzed, so as to provide corresponding analysis results and management implications.After identifying the deconstructing ideas of production operations, research data of industrial enterprises, and research methods of machine learning, we combine them for data analysis and continue to explore how enterprises should practice digitalization transformation to effectively improve green innovation performance. Digitalization features data is clustered using the DBSCAN algorithm to identify enterprises with heterogeneous differences in digitalization performance. Using SAX to classify decision attributes, and combining the clustering results, a preliminary analysis was made on the impact of enterprise digitalization on green innovation performance. Furthermore, on the basis of identifying enterprises with heterogeneous digitalization performance and grading green innovation performance, the nonlinear effects of different digital feature configurations on green innovation performance are explored using CART. Combined with the analysis results of clustering and decision tree, we also do a horizontal comparative analysis between different decision trees, and continue to mine the information hidden in the data.For the purpose of verifying that each digitalization feature has an impact relationship on green innovation performance under the perspective of production operation, a scatter diagram of the relationship among all features and green innovation performance is drawn. As shown in In order to continue to analyze the complex impact of enterprise digitalization features' configuration on green innovation performance from the perspective of production operation, and to obtain targeted conclusions, it is necessary to cluster enterprise digital data. As mentioned above, the DBSCAN contains two variables, eps and MinPts, that need to be preset. When eps is set to 0.5 and MinPts is set to 100, the research data are aggregated into two clusters with the principle of similarity within the group and dissimilarity between the groups, which is a stable state that is hard to continue subdividing. To verify the accuracy of the clustering results, the elbow method is also used to calculate the silhouette coefficient, and finally arrive at the suitable number of clusters as being two.Due to the fact that different features groups obtained by DBSCAN may have differences between groups, it is necessary to continue to analyze the clustering results. In order to analyze how enterprise digitalization affects green innovation performance under different clusters, and to meet the requirements of decision tree algorithm operation data, it is necessary to grade green innovation performance. The specific steps are as follows. First, the data that deviates too much from the normal range is removed to avoid the impact of extreme values on the rank division. The lowest green innovation performances are 0, which belong to the majority, and data with a large deviation from the normal range are all extremely high values, so the final green innovation performance used for grading ranges from 0 to 4.4. Second, the α of SAX is set as 2 to discretize the remaining data, thus making 0.6 the threshold to divide the green innovation performance into two grades: high and low, which means the green innovation performance lower than 0.6 is classified as low, otherwise it is high. Third, all extremely high values which were deleted previously are set to high-grade, thus completing the classification of green innovation performance.Through the analysis of the clustering results of enterprise digitalization features and the grades of green innovation performance, the following findings can be obtained. There are 1,516 enterprise data with high green innovation performance of excellent digitalization enterprises which accounting for 57.7% of the number in this cluster. In addition, there are 2,199 items of high green innovation performance of general digitalization enterprises which accounting for only 39.8% of the total number in this cluster. Therefore, enterprises with a higher degree of digitalization transformation can usually achieve high green innovation performance, which also means that digitalization has a positive impact on green innovation performance.In order to find the promotion paths and feature configurations of different enterprises' digitalization on green innovation performance, and to verify the phenomenon shown by the scatterplot, the CART decision tree algorithm is used to mine decision rules. On the basis of completing the cluster analysis, the 6 digitalization features are taken as conditional attributes and the discretized green innovation performance is used as the decision attribute to respectively construct decision trees for the cluster to extract decision rules. Besides, the decision rule tables and corresponding decision tree diagrams are also drawn to clearly display the decision rules.As shown in Confidence of 86.5% and 67.1% in order. This indicates that among the group of excellent digitalization enterprises, enterprises with higher digitalization innovation output are more likely to achieve higher green innovation performance. In addition, the high-grade Lift of I1 and I2 are respectively 1.50 and 1.16, which indicate that as the proportion of digitalization innovation output increases, green innovation performance may be promoted accordingly. The Support of these two promotion paths are respectively 12.5 and 17.9%, which further support the above analysis results to a certain extent. For excellent digitalization enterprises, their own digitalization transformation process has been relatively advanced, so the activity of paying attention to digitalization innovation output can be regarded as the icing on the cake. Both digitalization innovation output and green innovation performance belong to the output of enterprise innovation activities, so they have certain homogeneity in required resources, knowledge, and other aspects, and also have similarities in the affected steps and mechanisms. Therefore, it can be considered that enterprises that attach importance to the digitalization innovation output are likely to receive a positive response from green innovation performance, resulting in a “ripple effect.”In excellent digitalization enterprises, digitalization innovation output is the primary and positive digitalization feature of green innovation performance. According to Support of I3 is only 7.8%, the high-grade Lift of this promotion path is also 1.18, and the Confidence is 68.6%, which is higher than the Confidence of the directly connected parent node. The above phenomenon shows that with the promotion of these two digitalization features, the negative impact on green innovation performance due to the low digitalization innovation output can be alleviated. Besides, if digitalization innovation input and digitalization operation output are both low, it will only further aggravate the dilemma of low green innovation performance due to low digitalization innovation output. First, even if the green innovation performance of promotion path I4 is classified as high, and Confidence is 50.3%, but only the digitalization operation output in this promotion path is high, and its high-grade Lift is 0.87, which is less than 1, this would indicate that there is a negatively correlated influence relationship between this path and the green innovation performance. Second, all features of I5 are relatively low, and the final grade of green innovation performance is also low, with a Support of 16.8%, and the corresponding low grade Confidence of 58.8%, which is significantly different from the original high-low grade ratio of excellent digitalization enterprises. Moreover, the low grade Lift of I5 is 1.29, which is worse than the green innovation performance of I4. Digitalization innovation input is also a type of innovation activity, so enterprises that give resources and attention to digitalization innovation will also exert a positive impact on green innovation. Further, if an enterprise obtains sufficient revenue and financial performance from digitalization, it may be enough to relieve certain financial pressure, thereby helping enterprises to develop and innovate economically, and positively promote green innovation performance. Higher digitalization operation output means that the enterprise is more dependent on digitalization products to obtain income, and this type of product is mostly intangible or virtual, which helps the enterprise reduce the pressure on inventory and the supply chain, thus helping it to improve green innovation performance on the supply chain. In short, these two digitalization features will help the green innovation performance of such enterprises to further improve, thus jointly forming the direction of green digitalization transformation.Digitalization innovation input and digitalization operation output are both positive factors that influence green innovation performance of such enterprises, and are relatively more important than other factors. In I3, when the digitalization innovation input or digitalization operation output is high, there is a possibility for the enterprise to achieve a high-grade in green innovation performance. Although the To continue exploring the complex impact of digitalization on green innovation performance in the general digitalization enterprise, the same steps are used to build a decision tree. As shown in Lift of this path is 1.53, indicating that digitalization innovation output has a significant positive effect on green innovation performance. The high-grade Confidence of this path is 60.8%, which also significantly increases the proportion of high-grade states of green innovation performance in this cluster. However, the Support of II1 is only 4.8%, indicating that these types of enterprises are less likely to achieve high green innovation performance by virtue of this path. For general digitalization enterprises, as long as the feature of digitalization innovation output is high, it is still possible to promote the green innovation performance by virtue of the similarity of input resources and innovation process among different innovation activities.In the general digitalization enterprises, digitalization innovation output is also the primary and positive digitalization feature of green innovation performance. Digitalization innovation output is located at the top node of the decision tree, indicating that this feature has the most abundant information and the greatest influence on green innovation performance in this group. There is a decision path II1 in the tree, that when the enterprise obtains higher digitalization innovation output, it will make the final classification of this promotion path high. The high-grade Confidence of this path is 53.0%, and the high state Lift is also 1.33, which indicate that digitalization management and digitalization operation output have a positive impact on green innovation performance. However, the Support of II2 is only 3.0%, indicating that only a few enterprises can rely on this path to achieve high green innovation performance. Combined with the analysis of decision rules of excellent digitalization enterprises, enterprises should boost the overall digitalization process in order to more effectively promote green innovation performance. In addition, there are also two promotion paths for such enterprises, II3 and II4, and the corresponding green innovation performances are all low grades. Their corresponding Confidence scores are 60.5% and 67.4%, and the low grade Lift scores are respectively 1.01 and 1.20. This phenomenon shows that general digitalization enterprises need to rely on the promotion of digitalization management and digitalization operation output to achieve high green innovation performance. The Lift of path II3 is close to 1, which indicates that this path is difficult to change the fact that these types of enterprises were originally in a state of low green innovation performance, but at least it can ensure that the classification of green innovation performance will not be deteriorated. Based on the above analytic result, it can be seen that because of strong digitalization management, the attitude of enterprises toward digitalization transformation will be more positive, and the overall management mindset of enterprises will move toward more digitalization, and become flatter and more transparent. Moreover, a relatively higher digitalization operation output may help reduce inventory to achieve green innovation on the supply chain, and to have more power to promote green innovation performance.In this type of enterprise, digitalization management (DM) and digitalization operation output (DOO) are both positive factors that promote the green innovation performance (GIP), and they are also relatively important digitalization features compared with other factors. In other words, if this type of enterprise pays attention to digitalization management and digitalization operation output, it can alleviate the green innovation performance damaged by the lower digitalization innovation output. As shown in It is not comprehensive enough to analyze only a single decision tree, so it is necessary to make a horizontal comparison of different digitalization transformations. Through the horizontal comparative mining of the above two sets of decision trees, the following hidden information was also found.Confidence and Support are lower, while in excellent digitalization enterprises the results are the opposite. This phenomenon further shows that enterprise digitalization has a positive impact on green innovation performance, and the positive impact of various digitalization features of enterprises with a higher overall digitalization level will be stronger. With the improvement of the digitalization transformation, the ability of enterprises to perceive the external environment is enhanced, and the ability to seize innovation opportunities is also strengthened  The more advanced digitalization transformation the enterprises have, the more possible for high green innovation performance to be achieved. This conclusion echoes the existing research, and deepens the study on the topic between digitalization and green innovation together from the micro perspective . From th(2) Digitalization innovation is the digitalization element with the strongest influenceability on green innovation performance. From heterogeneous decision tree analysis results, digitalization innovation output is the most important digitalization factor that causes enterprises to improve green innovation performance, which has the strongest promotion effect on enterprises in different digitalization transformation processes. Moreover, for enterprises with more advanced digitalization transformation, digitalization innovation input is another focus deserving attention. Both digitalization innovation and green innovation belong to the category of innovation in essence, so they require similar input resources and their outputs are of considerable value. Therefore, enterprises attaching importance to digitalization innovation will have a “ripple effect” on green innovation.(3) With the advancement of digitalization transformation, enterprises should also focus on digitalization innovation input and digitalization operation output, otherwise they should focus on digitalization management and digitalization operation output. Compared with optimal distinctiveness thinking, enterprises with different digitalization situations have unique digitalization transformation paths that help improve green innovation performance. This research shows that, in addition to improving digitalization innovation output, ensuring sufficient digitalization operation output and digitalization innovation input are feasible ideas for enterprises that have excellent digitalization already to help improve green innovation performance. In contrast, enterprises with a relatively poor performance in digitalization are not advised to place too much emphasis on digitalization innovation investment, but should pay more attention to digitalization management, so as to start a top-down digitalization transformation to help improve green innovation performance. Therefore, with a clear understanding of their own digitalization transformation situation, enterprises should determine whether the focus should be digitalization innovation input or digitalization management, so as to help improve green innovation performance with their unique digitalization transformation process.The conclusions of this research can also provide the following implications for enterprise managers and policy makers about how enterprises should practice digitalization transformation to help improve green innovation performance.Enterprise managers can learn from the digitalization deconstruction method proposed in this research to comprehensively and systematically understand their own digitalization transformation process and evaluate their own digitalization competitiveness. While pursuing green innovation performance, enterprises must actively improve their digitalization level, and they need to pay special attention to digitalization-related operating income and innovation output. For enterprises that have achieved certain achievements in digitalization, in addition to paying attention to the above two digitalization features, they should also strengthen the injection of digitalization-related innovation resources to achieve the “ripple effect” on their green innovation performance. Enterprises that lag behind in the process of digitalization transformation should turn their emphasis to digitalization management, make its “mindsponge” mechanism closer to digitalization, and help further the development of green innovation. Moreover, the pharmaceutical industry and the food industry are closely related to public health, so we combine our research with that of He et al.  and ThøgFor policy makers, when promulgating laws and regulations related to environmental protection and green innovation, they should encourage enterprises to promote digitalization technology in production processes, management methods, innovation processes, and other aspects. Governments should also actively build a digitalization innovation environment and create a digitalization innovation atmosphere, so as to assist enterprises in green innovation and fulfilling their necessary social responsibilities. For enterprises that are undergoing a digitalization transformation and attach importance to green innovation, the government can provide policy support such as subsidies to help enterprises adjust their attitudes and management methods toward digitalization. Administrations can also use other economic means to help enterprises consume digitalization products and help improve the performance of green innovation in the supply chain. In addition, the office management department that provides digitalization-related support to enterprises should not be too hasty, but ought to take a longer period of time to investigate enterprises that actively inject digitalization-related resources.This study provides new results for the topic of enterprise digitalization and green innovation through this research, but there are still the following areas for improvement. First, though digitalization transformation should be implemented by all enterprises, another focus of this study is green innovation performance. Therefore, we only discuss industrial enterprises, not those in the service industry. When other scholars in the future study digitalization-related topics, they can expand the research samples of other industries, and continue to explore the complex relationship between enterprise digitalization and other variables. Second, the conclusion has already mentioned that digitalization innovation input and output, as well as digitalization management and digitalization operation output, are of great significance to green innovation performance. However, due to space constraints and the focus of this study, the above features cannot be further subdivided. In the future, researchers can continue to decompose the above-mentioned digitalization features and explore their possible links with green innovation of enterprises. Third, we only lock the time span at one lag period between digitalization features and green innovation performance, but it is not discussed here due to method and space limitations. In the future, researchers can consider expanding the sample and introduce time factors as much as possible to deepen the related research.The original contributions presented in the study are included in the article/supplementary material, further inquiries can be directed to the corresponding author.HL: conceptualization, methodology, design of the research, and supervision. HT: data curation, validation, design of the research, and writing the original draft. WZ: writing-review and editing and supervision. XW: writing-review and editing, validation, and supervision. All authors contributed to the article and approved the submitted version.This research has been supported by the National Natural Science Foundation of China (71771094), the Project of Natural Science Foundation of Fujian Province of China (2019J01067), and the Project of Social Science Planning of Fujian Province of China (FJ2020B088).The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


The textile industry is generating great environmental concerns due to the exponential growth of textile products’ consumption (fast fashion) and production. The textile value chain today operates as a linear system , thus putting pressure on resources and creating negative environmental impacts. A new textile economy based on the principles of circular economy is needed for a more sustainable textile industry. To help meet this challenge, an efficient collection, classification, and recycling system needs to be implemented at the end-of-life stage of textile products, so as to obtain high-quality recycled materials able to be reused in high-value products. This paper contributes to the classification of post-consumer textile waste by proposing an automatic classification method able to be trained to separate higher-quality textile fiber flows. Our proposal is the use of near-infrared (NIR) spectroscopy combined with a mathematical treatment of the spectra by convolutional neural networks (CNNs) to classify and separate 100% pure samples and binary mixtures of the most common textile fibers. CNN is applied for the first time to the classification of textile samples. A total of 370 textile samples were studied—50% used for calibration and 50% for prediction purposes. The results obtained are very promising (100% correct classification for pure fibers and 90–100% for binary mixtures), showing that the proposed methodology is very powerful, able to be trained for the specific separation of flows, and compatible with the automation of the system at an industrial scale. The textile industry is fundamental for society and everyday wellbeing . Different textile products are needed, such as household products and personal clothes. Textile consumption has been exponentially growing in the last decade (having doubled its production around the world in the last 15 years) due to low costs and fast fashion . Thus, lProduction of textile products generates great environmental impacts along their value chain, including production of raw materials, manufacturing, and the products’ end of life . The maiAt their end of life, textile products can be reused, remanufactured, recycled, incinerated (with energy recovery), or landfilled. The degree of processing and the benefit for the environment vary from one option to the other . AlthougTextile recycling rates (including reuse and remanufacture) account for only 13% of world clothing, and usually this small recycled amount is used in lower-value applications . RecycliAccording to European legislation (Directive (EU) 2018/851) , selectiTherefore, it is necessary to implement an automatic classification system to allow the correct separation of the different textile fibers, thus increasing their recyclability and the added value of recycled textile materials. This system will contribute to moving from the current linear textile system to a more circular one. Today, the classification of textiles is mainly performed manually (with high human resource costs) and rarely automated; thus, it is not possible to treat large amounts of waste each day .The contributions of this paper are to the development of a sorting system able to separate the most common pure textile fibers , as well as binary mixtures of viscose–polyester and cotton–polyester. The proposed sorting method is compatible with an automatic textile classification approach for textile fibers, and is based on a novel mathematical treatment  of the near-infrared (NIR) spectra. Algorithms based on convolutional neural networks (CNNs) are applied to classify textile samples; NIR spectra can be registered through optical fiber sensors in a continuously working process, thus allowing the large-scale recycling of post-consumer textile waste. It should be noted that this approach can be applied for classifying and sorting many other materials, including plastics, paper, or leather, among others.The near-infrared (NIR) region has a range from 750 to 2500 nm. The absorption of radiation is due to the combination of overtones and bands from the fundamental vibrations produced in the mid-infrared region . These bx groups or combination bands of these groups. They are mostly due to overtones of the CH, OH, and NH groups, although in some cases the bands corresponding to PH and SH groups can be observed.Almost all of the absorption bands observed in NIR come from overtones of the stretching vibrations of AHThe high degree of bands overlapping and the complexity of the spectrum, together with its dependence on the sample’s physical state, make it difficult to perform simple calibrations (at one or a few wavelengths) when working with the NIR technique. Thus, the use of complex mathematical methods is needed to extract useful information from the spectrum. On the other hand, its dependence on chemical and physical parameters requires the calibration to be very complete, and to take into account all of the expected variability in the samples.One of the great advantages of NIR spectroscopy is its versatility and adaptability to analyze samples of different natures. For solid samples, the measurement is carried out by diffuse reflectance. NIR radiation has the ability to significantly penetrate a solid sample and acquire important information. This is one of the characteristics that make this form of radiation an especially useful tool in the study of solids, as chemical information can be obtained in a non-invasive and non-destructive way.When measurements are carried out by diffuse reflectance, the use of an optic-fiber probe allows the NIR spectrum to be recorded directly on the sample, without any previous treatment, achieving a remarkable saving of time and avoiding the use of polluting reagents.NIR spectroscopy has experienced a strong advance in recent decades, motivated both by the development of new instruments—which allow the rapid recording of the full NIR spectrum—and by the implementation of mathematical methods for the processing of spectral information. Once calibrated, the NIR spectra can be used for the fast determination of the properties of the analyzed sample . Thus, tThus, the aim of this paper is to develop an industrially scalable sorting method to classify textile waste (composed of pure fibers and binary mixtures) by registering the NIR spectra of the samples, followed by a mathematical treatment using convolutional neural network (CNN) algorithms.TM Optiprobe Analyzer, FOSS AnalyticalA/S, Hillerod, Denmark) of such samples using appropriate mathematical methods. These two approaches are summarized in This paper analyzes the accuracy of two approaches for classifying unknown incoming fiber samples, based on a supervised strategy by analyzing the NIR spectral data  and prediction data subsets . This stFor a fair training process and a good assessment of the results attained, the pertinence class of the whole set of samples must be known. Then, the samples belonging to the calibration subset must be labelled, with the labels assigning each sample to its class , since the proposed approach is a supervised training process. Although in this paper the pertinence class of the samples in the prediction subset is already known, because it is necessary to assess the performance of the classification process, in general, the pertinence class of the prediction samples is not known, and the classification method must accurately estimate their pertinence classes.It is a common practice to process the raw spectral data provided by the spectrometer  to increThe usual data processing consists of obtaining the first or second derivative of the spectra. Derivatives are applied with the aim of enhancing spectral differences. They act as a specific type of baseline correction, removing constant background signals at the cost of increasing noise. Derivatives are applied for enhancing the visual resolution, highlighting spectral structures, and resolving overlapping peaks. It is known that each derivative stage decreases the polynomial order by one. Whereas the first derivative allows the removal of a constant offset, the second derivative also removes a linear term of the spectrum . The derNext, the columns of the data matrix are mean-centered to remove any bias, so that afterwards the mean-centered columns have zero mean, and their variances remain unaltered.Prior to the application of the CNN it is possible to apply dimensionality reduction methods, where dimensionality refers to the number of input variables or wavelengths in the analyzed problem. These methods aim at reducing the number of variables in the problem by combining the original variables into a condensed set of new variables. This step is important because NIR spectra include thousands of wavelengths per sample, and it is known that a large number of variables can reduce the performance of classification algorithms .The new variables, also known as latent variables, are usually obtained by linearly combining the original variables, with the aim of retaining the same information and rejecting the inherent noise contained in the original variables.v is a vector, m is the number of wavelengths in the NIR spectra, and B is the interclass dispersion matrix, defined as follows:W is the intraclass dispersion matrix, defined as follows:i = 1,2, … c, ijx represents the elements of the spectral data matrix Xn,m) dimensionality reduction technique is applied. It condenses the significant spectral data into a small set of latent variables ,28 calcuV, the new latent variables or canonical variates (CVs) can be determined as Yn,s), while the number of samples is usually smaller. Therefore, it is imperative to apply a suitable algorithm for reducing the dimensionality of the problem, i.e., the number of variables before CVA can be applied. To this end, principal component analysis (PCA) is applied before applying the CVA algorithm, because PCA is probably the most recognized unsupervised algorithm for dimensionality reduction . PCA allBy applying the PCA + CVA sequence, the dimensionality of the problem is greatly reduced, to obtain a number of latent variables (CVs) equal to the number of classes minus one. Once the CVs are calculated, the CNN algorithm can be applied to classify the unknown textile samples, although the calculation of the PCA + CVA sequence is optional.Artificial neural networks have shown their potential for successfully predicting the structural properties of textiles . CNNs arCNNs usually have three main parts, i.e., an input layer, a hidden layer, and an output layer ,38. The The convolutional layer is a fundamental part of the neural network, because it extracts the relevant information from the input data  by applyA CNN requires a specific data structure, so it is necessary to accommodate the input data to the requirements of the CNN. Therefore, the input data are fed to the CNN as a matrix, where the columns are the wavelengths of the spectral data, while each row corresponds to a different sample. The output of the CNN is a classification matrix, where every column corresponds to the probability of the sample belonging to each of the defined classes, and every row refers to a different sample.The next step is to define the structure of the CNN. In this case, two convolutional layers with the subsequent pooling layers were used, as shown in ® automatically stops when reaching any of the three following conditions: reaching a fixed number of iterations (30 by default), a fixed time (no time limit by default), or a stopping criterion.Two convolutional layers were selected after training CNNs with different numbers of layers, this solution offering a good balance between computational burden and accuracy . The AdaThis paper analyzes textile fibers obtained from several catalogs of different companies. The analyzed samples (textile fibers) included natural, synthetic, and artificial fibers, as well as combinations of natural and synthetic/artificial fibers in different proportions. Natural fibers include silk, wool, linen, and cotton. Synthetic fibers include polyamide and polyester, while viscose is an artificial fiber. While artificial fibers are generated by transforming natural products , synthetic fibers are generated from petrochemical polymers. Hereafter, artificial and synthetic fibers will be referred to as manmade fibers.Study #1: To classify 100% pure textile samples among seven different classes .Study #2: To classify mixtures of viscose and polyester in different percentages.Study #3: To classify mixtures of cotton and polyester in different percentages.A sequence of three studies was designed. For each study, the selected textile fibers were chosen to include maximum variability, so different presentations (fabric or yarn) and a multitude of colors (from dark to light) were included in the analyzed set of samples. The three studies are described below see :Study #1Samples were provided by different companies  in a span of 4 years (2016 to 2019). A total of 52 different commercial catalogues together with additional spare samples provided by the companies was used to obtain the resulting samples for each of the three studies. The number of catalogues used in each study is presented in TM OptiProbe Analyzer provided by FOSS AnalyticalA/S, Hillerod, Denmark.) controlled by the Vision SoftwareTM was used. Although this was able to acquire the spectra in the 400–2500 nm range, the 1100–2200 nm range was analyzed to reduce the effect of noise and to avoid the visible region. To further reduce the effect of noise, the spectral data were acquired through averaging of 32 scans. Since the resolution of the FOSS XDSTM OptiProbe Analyzer is 0.5 nm, each spectrum has 2201 points per sample. Next, the raw data provided by the NIR spectrometer were processed and transformed by means of the mathematical methods detailed in This study is based on the NIR spectral data of the analyzed textile fibers. To acquire the NIR spectra of the studied samples, an NIR spectrometer equipped with a reflectance fiber-optic module  are shown in In this section, three studies with different textile samples are presented to prove the accuracy of the proposed approach.This first study analyzed 210 pure textile samples, i.e., the samples were made of pure fibers, as summarized in CVA sequence prior to the CNN, as described in As shown in The results in This second study analyzed 73 mixed textile samples, which included pure viscose and viscose/PE mixtures, as summarized in As shown in As seen in This third study analyzed 90 mixed textile samples, which included pure cotton and cotton/PE mixtures, as summarized in As shown in As seen in The main conclusion of the present work is that the developed method (using a near-infrared (NIR) sensor and mathematical treatment based on convolutional neural networks) allows us to correctly classify pure textile fibers  and binary blends .The results in The best classification was obtained with the consecutive application of the PCA + CVA + kNN methods, achieving 100% correct classification rates for pure samples in absorbance mode, 100% correct classification rates for viscose/polyester blends (viscose/PE) in first derivative mode, and 91.1% for cotton/polyester blends (cotton/PE) in second derivative mode.It should be remembered that in the latter case the three classes defined for the cotton/polyester blends (cotton/PE) were very broad and similar: cotton > 97%; cotton 70–90%; and cotton 30–65%. In contrast, the viscose/PE mixtures were three narrower groups, and were more separated from one another: viscose 100%; viscose 90%; and viscose 70–75%.The method is powerful, and can be trained to separate pure fibers and the most interesting binary mixtures; its only limitation is the need for a sufficiently large database with samples of known composition. This can help the textile industry to be more circular, by obtaining secondary textile polymers and reducing the need for natural  and synthetic  virgin materials.