Houses mean a lot to Chinese people, and in the context of the urban-rural dualist system, town housing has a special significance for rural-urban migrants. Based on the 2017 China Household Finance Survey(CHFS) data, this study uses the Ordered Logit (OLogit) model to empirically test the effect of owning commercial housing on the subjective well-being(SWB) of rural-urban migrants, and through the mediating effect and moderating effect to conduct an in-depth investigation into the intrinsic effect mechanism and further explains the relationship between the two and the current residential location of their family. The results of the study show that: (1) Owning commercial housing can significantly enhances the subjective well-being(SWB) of rural-urban migrants, and the findings remain robust after using alternative model, adjusting the sample size, correcting for sample selectivity bias using propensity score matching(PSM), and controlling for potential endogeneity bias combining instrumental variables and conditional mixed process(CMP); (2) The effect of owning commercial housing on the subjective well-being(SWB) of the first generation rural-urban migrants, rural-urban migrants in the eastern and central regions, and those who obtained housing before the rapid rise in house prices is more pronounced; (3) Commercial housing acts on the subjective well-being(SWB) of rural-urban migrants through the mediating effect of housing assets, and there is some regional variation in the mediating effect of housing assets. At the same time, the household debt acts as a positive moderator between commercial housing and the subjective well-being(SWB) of rural-urban migrants; (4) Even with commercial housing, rural-urban migrants whose families are currently living in rural areas still have a stronger sense of subjective well-being (SWB). Well-being is everyone’s aspiration for a better life. It has become a major indicator of quality of people’s livelihoods , gradualWith the introduction of the "Easterlin paradox", more scholars began to explore happiness, thus creating a new field of economics: the economics of happiness. At present, domestic and foreign scholars’ research on happiness mainly focuses on four aspects: economic factors, political factors, socio-demographic factors and environmental factors. In recent years, global housing prices have been rising, and coupled with the fact that housing issues affect the national economy and people’s livelihood, more and more studies have begun to focus on the impact of housing on people’s SWB, mainly in terms of housing ownership –8, housiIn addition, academics have summarized several possible channels through which housing affects the SWB of residents, including household asset portfolio and level of wealth accumulation , communiMore importantly, the location of urban residents’ commercial housing is mostly the current residence of their families, and their main economic and social activities are also in the urban. However, for rural-urban migrants, they are still in the state of "peri-urbanization". Even if they have commercial housing, the main members of their families may still live in rural areas, which is obviously different from urban residents. So what is the relationship between commercial housing, the current residential location of the family and the SWB of rural-urban migrants? This question has not been answered in detail.To answer these research questions, this paper uses data from the 2017 China Household Finance Survey (CHFS) to empirically test the effect of owning town commercial housing on the SWB of rural-urban migrants. This research makes four possible contributions to the literature. First, the research perspective is novel. This study focuses on the impact of owning commercial housing on the SWB of rural-urban migrants, enriching the existing literature on housing and the SWB of residents, complementing the research sample of the group of rural-urban migrants, verifying whether owning commercial housing by low-income groups such as migrant workers can still improve their SWB, and making a complete chain of evidence available for empirical research on the topic of housing and SWB; Second, the empirical strategy is rigorous. This study uses propensity score matching (PSM) to correct for selectivity bias and combines instrumental variables and conditional mixed process (CMP) to better overcome potential endogeneity bias, ultimately yielding robust analytical results; Third, the underlying impact mechanism is explored. From the perspective of housing assets and household debt, this study also focuses on analyzing the underlying mechanism of the effect of commercial housing on the SWB of rural-urban migrants and the regional differences in such mechanism; Fourth, this study also further discusses the relationship between commercial housing, current residential location of their family and the SWB of rural-urban migrants in China’s “semi-urbanization” state, and finds that they have not completed the family-oriented migration.Research on the relationship between homeownership and the SWB has been receiving extensive attention from scholars across countries. A large body of research literature has concluded that homeownership can enhance the SWB of residents –7. Fong Rural-urban migrants are a unique type of floating population in China. Rural-urban migrants who own a home in an urban area are significantly happier than those who do not. Those who own a home in a rural area are not significantly happier than those that do not . Given tHypothesis 1.Rural-urban migrants who own commercial housing have stronger SWB than those who do not.Housing is characterised by multiple attributes, both residential  and investment . In terms of asset attributes, housing as a fixed asset , housingHypothesis 2.Commercial housing affects the SWB of rural-urban migrants through housing assets. That is, housing assets play a partial mediating effect.third hypothesis is proposed as follows:Debt has both positive and negative effects on the SWB. On the one hand, according to the liquidity constraint theory, when households face liquidity constraints (such as a large housing expense), borrowing can smooth out current consumption, thereby improving the satisfaction of current economic life and thus increasing one’s sense of the SWB . On the Hypothesis 3.The SWB of rural-urban migrants who own commercial housing is affected by the moderating effect of household debt.fourth hypothesis is proposed as follows:People in the Western world generally report lower levels of SWB in urban areas than in rural areas . FurtherHypothesis 4.After owning commercial housing, compared with towns, rural-urban migrants whose family’s current residential location is in rural areas still have a greater sense of SWB.Based on the above assumptions, a path analysis diagram of the impact of commercial housing on the SWB of rural-urban migrants is drawn, as shown in hukou are considered to be migrants, and we define migrants with rural hukou elsewhere as rural-urban migrants. On this basis, the rural-urban migrants who owned their houses were retained, and samples with missing and invalid values in the variables were eliminated. The remaining sample size becomes 5477. In addition, provincial  data from the statistical database of China Economic Network.The China Household Finance Survey (CHFS) is a nationwide sample survey project conducted by the China Household Finance Survey and Research Centre of Southwestern University of Finance and Economics. The baseline survey was launched in 2011 and four follow-up surveys have been successfully implemented in 2013, 2015, 2017 and 2019 respectively. Using a stratified three-stage probability proportion to size (PPS) random sampling method, the CHFS data includes information related to housing assets and financial wealth, debt and credit constraints, income and consumption, social security and insurance, demographic characteristics and employment, and subjective attitudes, filling a gap in academic research and the current state of household finance in China, with a high degree of data representativeness. This study uses 2017 CHFS data , and the survey sample covers 29 provinces , 355 counties , and 1428 village (neighborhood) committees across China, with a sample size of 40011 households and 127012 individuals. According to the research purpose, this paper merged family data and personal data. We have particular interest in the relationship between commercial housing and the SWB of rural-urban migrants. Using counties  as a unit, individuals without local Researchers have carried out a lot of research on the relationship between housing and SWB. And there are many statistical databases that can measure the variable of housing and SWB, including Chinese Social Survey (CSS), Chinese General Social Survey (CGSS), China Migrants Dynamic Survey (CMDS), China Household Finance Survey (CHFS), etc. However, in addition to caring about how commercial housing affects the SWB of rural-urban migrants, we also focus on exploring the underlying mechanism from the perspective of housing assets and household debt and expanding the research on current family residential location and SWB of rural-urban migrants with commercial housing in this study. The advantage of the CHFS database is that it not only includes the dependent variable of SWB, the independent variable of commercial housing, but also has many other related required variables, such as the demographic characteristics, household characteristics, socio-economic characteristics, social security and subjective attitude. And more importantly, it has the rich information in terms of assets and debts, which provides important data support for our research purposes. Therefore, we choose CHFS data for research.Subjective well‑being (SWB). The CHFS questionnaire asks respondents "In general, do you feel happy now?" Respondents were given a five-point Likert scale of integers from 1 to 5, representing "very happy", "happy", "neutral", "unhappy" and "very unhappy". Based on the needs of the study, they were reassigned to a scale from 1 to 5 to represent "very unhappy" to "very happy" respectively. The survey results show that the average SWB of rural-urban migrants is 3.84, which is between neutral and happy, 49.06% of the samples answered happy, and 20.18% answered very happy. Overall, 69.24% of rural-urban migrants considers themselves happy . The CHFS questionnaire asks the respondents about their housing situation, the value for this variable is based on the questions "How was the house acquired?" and "What is the form of ownership of this house?" to determine whether a rural-urban migrant owns commercial housing. Rural-urban migrants who answered "buying new commercial housing" or "buying second-hand commercial housing" and those who answered "property form is commodity housing" will be assigned a value of 1, otherwise they will be assigned a value of 0 , inheritance or donation, purchasing from unit below market price , fund-rasing building, self-establish/expansion, settlement housing (compensation for collection of land), purchasing houses with limited property rights and Others. And the second question has seven other options: policy housing , purchased public housing , housing for using only (public housing controlled by government or units), house-site housing on collective-owned land, houses with limited property rights (on collective-owned land), commercial & residential house and Others. These answers are all considered as not owning commercial housing.). The survey results show that the proportion of rural-urban migrants owning commercial housing is 35.5%.Housing assets. Mediating effect refers to the effect of the independent variable (X) on the dependent variable (Y) through the mediator variable (M) . This paHousehold debt. Moderating effect means that the effect of the independent variable (X) on the dependent variable (Y) is moderated by the moderator variable (M) . This paTo ensure the scientific rigour of the study, on the basis of the existing research literature , 52–55, The SWB of rural-urban migrants is a typical discrete ordinal variable. This study therefore employs the ordered logit model to empirically analyse the effects of commercial housing on SWB of rural-urban migrants. The Ologit model setting in this study is shown in Formula :SWBBi=SWBi denotes the SWB of the ith rural-urban migrant; Commercial Housingi denotes whether the rural-urban migrant owns commercial housing; Zi indicates control variables; Areai denotes regional dummy variable; α、β、γ and δ denote theparameters to be estimated and εi denotes stochastic disturbance team.Where There is a consideration that whether a rural-urban migrant owns commercial housing may not satisfy random sampling, but a self-selection process determined by individual characteristics. That is, systematic differences in initial conditions  other than commercial "housing" If that is the case, direct regression may result in selectivity bias due to non-random sampling. For this reason, this study uses propensity score matching (PSM) to construct a counterfactual framework to correct for this.Di = {0, 1} to indicate whether the rural-urban migrant owns commercial housing. In our research, Di = 1 thus denotes that the rural-urban migrant own commercial housing, and Di =0 denotes that he do not. And there may be two states for the SWB (yi) of rural-urban migrants, yi1 indicates the SWB of rural-urban migrants who own commercial housing, while yi0 indicates the SWB of rural-urban migrants who do not own commercial housing.The Propensity Score, first proposed by Rosenbaum and Rubin , is the E(yi1|Di = 1)) and the SWB of rural-urban migrants who do not (i.e. E(yi0|Di = 1)) is calculated on the basis of satisfying the balance test and overlap assumption, that is, the value of ATT (Average Treatment Effect on the Treated). As shown in Eq (The principle of the propensity score matching (PSM) method is to use the logit model to calculate the propensity score p, also known as the probability p, of rural-urban migrants owning commercial housing based on observable covariates. The treatment and control groups are then matched according to p-score by different matching methods , thus eliminating sample selectivity bias and acting as a random experiment. Finally, the difference between the SWB of rural-urban migrants who own commercial housing (i.e. wn in Eq .E(yi0|Di = 1) cannot be observed, and the advantage of the PSM method is to find an effective control group E(yi0|Di = 0) to replace E(yi0|Di = 1) for rural-urban migrants who actually own commercial housing, so as to achieve "counterfactual" estimation.In fact, The propensity score matching (PSM) method can correct the selection bias caused by observable variables, but this study may also have endogeneity problems caused by omitted variables and reverse causality. Firstly, factors that are difficult to measure, such as the personal experience or psychological feelings of rural-urban migrants, may also affect their SWB, leading to the problem of missing variables; Secondly, the SWB of rural-urban migrants may affect their probability of owning commercial housing, that is to say, rural-urban migrants who are happier are more likely to live in towns and thus settle in towns, and are more likely to own commercial housing. Conversely, rural-urban migrants with a low sense of SWB may choose to return to rural areas and are less likely to own commercial housing. This creates a reverse causality problem.Potential endogeneity issues will lead to inconsistent estimated coefficients, which are commonly addressed by the instrumental variables method. Most current studies use higher area-level housing characteristics variables as instrumental variables for individual housing variables. In line with the relevant literature , 58 and In this paper, since the dependent variable of SWB is ordinal, and the endogenous variable of commercial housing is binary, neither two stage least square (2SLS) nor binary selection model (IV-Probit) is applicable. In order to estimate the instrumental variable model more effectively, we employ the conditional mixed process (CMP) to test for endogeneity. Referring to Roodman , the CMP3.3.4.1 Mediating effect model. The traditional procedure for testing mediating effects is the stepwise regression method proposed by Baron and Kenny [nd Kenny  , if θ = 0 means that the moderating effect is not present. If debt can strengthen the effect of commercial housing on the SWB of rural-urban migrants, then θ>0, otherwise, θ<0.Where This paper uses the software Stata14 for empirical analysis, the goal is to explore whether commercial housing ownership can improve the SWB of rural-urban migrants. First, with commercial housing as the independent variable, the Ologit model is used to estimate the impact of commercial housing on SWB; Then, the robustness of the estimated results is tested by replacing the model (replacing the Ologit model with the Oprobit model and OLS model) and adjusting the sample size. Moreover, considering that rural-urban migrants owning commercial housing may be the result of "self-selection", the PSM method is used to correct the selection bias for this reason, and the CMP method is used to test other endogeneity issues; Next, this study further analyses the heterogeneity of the impact of commercial housing ownership on the SWB of rural-urban migrants from two perspectives: intergenerational and regional differences; After this, in order to examine the mechanism of the effect of owningcommercial housing on the SWB of rural-urban migrants, a model of the mediating effect of housing assets and a model of the moderating effect of household debt are constructed to verify this. Lastly, we continues to extend the analysis of the impact of the current residential location of households of rural-urban migrants who have owned commercial housing on their SWB.2 value gradually increase from model 1 to model 3, and the χ2 value all pass the significance test at the 1% statistical level, and there is no significant change in the direction of influence and significance level of the independent variable after sequentially adding variables. These all indicate that the estimation results of the ologit model have strong robustness. Specifically, as shown in model 3, rural-urban migrants owning commercial housing can significantly improve their SWB. Therefore, hypothesis 1 is verified.Yip et al. [Regarding the results of the estimation of control variables, as shown in model 3, most of the variables also have a significant effect on the SWB of rural-urban migrants. (1) Among the demographic variables, compared with the second-generation rural-urban migrants, the first-generation rural-urban migrants have a stronger sense of SWB. The possible reason is that the first-generation rural-urban migrants already gained some capital and contacts in the places they moved to, and are gradually adapting to town life. With the improvement of education level, the SWB of rural-urban migrants shows a downward trend. The reason may be that the higher the level of education, the higher the demands on their quality of life, and thus the higher the threshold of their perceived happiness. This is consistent with the findings of Knight and Gunatilaka ; In addip et al. ; (6) In The regression estimation of the dependent variable as an ordinal variable can use the ologit model and oprobit model. The main difference between the two is that the dependent variable obeys different distributions, with the logit model following a standard logistic distribution, and the probit model following a standard normal distribution. Angrist and Pischke believe that under the condition of accurate model setting, whether OLS or ordinal probability model (Ologit/Oprobit) is used for regression, there is no difference in results . And FerThe paper further adjusts the sample size to test the regression results. As shown in The PSM method is used to correct for self-selection bias in the “ownership of commercial housing” of rural-urban migrants in order to reduce the endogeneity issues in the sample estimation. To improve the matching efficiency, this paper uses kernel matching method for propensity score matching (PSM). The balanced test of the matched results is shown in As shown in Apart from selection bias, this study may still face endogeneity problems such as omitted variables and reverse causality. To this end, this study combines instrumental variables and CMP method for analysis.The results of the CMP model report the endogeneity test parameter atanhrho_12 in addition to the correlation. When the atanhrho_12 parameter is significant, it can be considered that the baseline model has endogeneity. As shown in From the above baseline regression results we found that owning commercial housing significantly enhances the SWB of rural-urban migrants. However, the above results only represent the average effect on the SWB of rural-urban migrants, and do not take into account the intra-group differences among rural-urban migrants. Therefore, this study also analyses the heterogeneity of the effect of commercial housing on the SWB of rural-urban migrants from the perspective of intergenerational, regional and housing acquisition time. Specifically, the intergenerational classification includes the first generation and second generation. Consistent with the conventional definition, the first generation are those born before 1980, and the second generation are those born in the 1980s and after. Using the classification standard of the National Bureau of Statistics, the 29 provinces in CHFS are divided into eastern, central and western regions. In addition, housing prices in China remained comparatively stable before 2004 when the open auction for the transfer of land use rights was introduced by the government, but it began to soar after 2004 . So we tAs shown in From a regional perspective, owning commercial housing has a statistically significant positive impact on the SWB of rural-urban migrants in the eastern and central regions, but not in the western. The impact is higher in the east than in the center region. According to the interpretation of the National Development and Reform Commission, the division of China ’s eastern, central and western regions is a policy division, not an administrative division, nor a geographical concept division. Therefore, the eastern part refers to the provinces and cities that first implemented the coastal opening policy and had a high level of economic development; the central region refers to the economically sub-developed areas, while the western region refers to the economically underdeveloped areas. Furthermore, the level of economic development determines the level of housing prices, the quality of public services, employment opportunities, educational resources and migration willingness. Therefore, compared with the western region, the eastern and central regions are the main gathering places of population migration, and owning commercial housing in the central and eastern regions can better reflect their core competitiveness, symbolize their social status, and thus enhance their SWB, and this effect is most obvious in the eastern region.From the perspective of housing acquisition time, the effect of owning commercial housing on the SWB of the rural-urban migrants who obtained housing before 2004 is more pronounced. In other words, the happiness effect is more significant before the housing price changes. This result is not difficult to understand, rising housing prices lead to higher housing costs for rural-urban migrants to obtain commercial housing, which impacts their perception of SWB to some extent. However, before the rise of housing price, the housing price-to-income ratio was relatively low. At that time, acquiring commercial housing not only had a lower cost, but also brought the corresponding wealth effect due to the subsequent appreciation of housing, both of which enhanced their SWB.Consequently, the influence effects of the ologit model and oprobit model are decomposed according to the KHB method mentioned in the previous section, as shown in In addition, we take SWB as a continuous variable and then perform OLS regression on the mediating effect of housing assets. According to the sequence of stepwise tests of the mediating effect model, the regression coefficient of commercial housing on SWB is significantly positive from model 9, the regression coefficient of commercial housing on housing assets is significantly positive from model 28, and then the regression coefficient of housing assets on SWB is significantly positive from model 29, which indicates that the BK stepwise regression passes the test in turn. This proves that the variable of housing assets is one of the channels through which commercial housing affects the SWB of rural-urban migrants. At the same time, Bootstrap method is used to test the mediating effect again. 500 Bootstrap samples are drawn by random repeated sampling, and the estimated value of 500 coefficient products is obtained. As shown in The above results robustly verify the research hypothesis 2. That is, the asset attribute of commercial housing is an important path affecting the SWB of rural-urban migrants, and housing has a certain wealth effect. In addition, to explore the regional differences in the mediating effect of housing assets, we further analyse the sample of rural-urban migrants in the east, central and west respectively using the KHB method, and the regression results are shown in The mediating and total effects of housing assets in the eastern region are both significantly positive and the direct effect is not significant, indicating that housing assets play a fully mediating role in the eastern region. Although the results may be biased due to sample size, it is sufficient to show that the asset attribute of commercial housing in the eastern region is the main mediator of rural-urban migrants’ SWB. The mediating effect was significant in the central region, with a share of around 10%, which was lower than the share of mediating effect in the total sample. In the Western region, housing assets did not have a mediating effect. This finding is consistent with the actual situation in China and similar to the results of the heterogeneity analysis above. The eastern coastal region has a developed economy, more employment opportunities, higher levels of public services than the central and western regions, and relatively higher housing prices, and the fixed-asset effect of commercial housing in the eastern region brings a strong sense of SWB to rural-urban migrants.To ensure the robustness of the test results, three models of Ologit, Oprobit and OLS are used to analyse the moderating effect of commercial housing and debt. The regression results are shown in First, the results of model 3, model 6 and model 9 above suggest that commercial housing ownership can significantly improve the SWB of rural-urban migrants. Then, the moderator variable of debt is added to model 30, model 32 and model 34, and the interaction effect of commercial housing and debt is added to model 31, model 33 and model 35 in turn. We find that debt has a significant negative effect on the SWB of rural-urban migrants, while the coefficient of the interaction term is significantly positive. So we believe that debt plays a certain moderating role between commercial housing and the SWB of rural-urban migrants. In other words, debt can reinforce the positive effect of commercial housing on the SWB of rural-urban migrants, and Hypothesis 3 is verified.Why does debt instead increase the positive impact of owning commercial housing on the SWB of rural-urban migrants? In China, housing is the foundation of "living and working in peace and contentment", especially for rural-urban migrants who come to cities or towns from rural areas, the most basic motivation for them to own commercial housing is to have a place to live and settle down here, to enjoy high-quality public services such as medical care and education, and to improve their competitiveness in the marriage market. Owning commercial housing is an important part of China’s urbanization process. At the same time, most of the rural-urban migrants in this study have only one commercial housing and there are no other motives such as property speculation. Therefore, in this case, debt has a positive moderating effect on the relationship between owning commercial housing and the SWB of rural-urban migrants, which we interpret to mean that whether it is the housing loan or other borrowing that rural-urban migrants takes out in order to own commercial housing, these debts to some extent smooth out the consumption dilemmas arising from housing expenditure, thus positively moderating their SWB. Although owning commercial housing may increase household debt, it also means that these rural-urban migrants have relatively strong economic strength and can afford the corresponding debt, and more importantly, owning commercial housing also symbolizes the change in status and the realization of the "urban dream". Accordingly, this logic holds true for the relationship between owning commercial housing and the SWB of rural-urban migrants. This further supplements the research findings of Zheng et al. that there is no evidence of a correlation between financial constraints and SWB decline among those with homeownership .It has been confirmed above that rural-urban migrants whose families currently live in rural areas are more happier . But this result is only an overall effect and does not distinguish impact for rural-urban migrants who own commercial housing versus those who do not. Therefore, this study further explores the heterogeneous effect of the current residential location of households in these two groups on the SWB of rural-urban migrants. It can be seen from So why has owning commercial housing failed to reverse rural-urban migrants’ perceptions of the SWB when their families live in towns? The possible reason is that the cost of urban living is still the primary problem of rural-urban migration, compared with living in rural areas, family-oriented urban migration will bring huge economic burdens. In addition, their social interactions are usually on blood and local ties, out of the familiar living environment will make them feel insecure, and it is relatively difficult to reconstruct the social network in cities and towns, rural-urban migrants’ families have not really integrated into urban life. As a result, even with commercial housing, their families live in rural areas makes rural-urban migrants feel happier. The family-based urbanization process still has a long way to go.Based on the data of China Household Finance Survey in 2017, we use Ordered Logit model and take rural-urban migrants as the research object to explore the relationship between owning commercial housing and SWB of low-income groups, and that the heterogeneous impact on the intergenerational, regional and housing acquisition time, and then explore the underlying mechanism of its impact from the perspective of housing assets and household debt. Finally, we further expand the research on the current living location of families, commercial housing and the SWB of rural-urban migrants. The results show that: Firstly, commercial housing significantly enhances the SWB of rural-urban migrants. The findings remain robust after replacing the model, adjusting the sample size, correcting for sample selectivity bias using propensity score matching (PSM), and controlling for potential endogeneity bias by combining instrumental variables and CMP estimation method. It is proved that the positive effect of commercial housing on SWB still exists in low-income groups; Secondly, the effect of owning commercial housing on the SWB of the first generation rural-urban migrants, rural-urban migrants in the eastern and central regions and those who obtained housing before 2004 is more pronounced. This result is consistent with China’s economic development level and development process; Thirdly, commercial housing acts on the SWB of rural-urban migrants through the mediating effect of housing assets, and the mediating effect accounts for 31.1% of the total effect, especially in the eastern region, housing assets act as the main mediation. And the household debt acts as a positive moderator between commercial housing and the SWB of rural-urban migrants. In other words, the SWB of rural-urban migrants with commercial housing is not reduced by household debt; Fourthly, even with commercial housing, rural-urban migrants whose families are currently living in rural areas still have a stronger sense of SWB. The cost of urban living is still the primary problem of rural-urban migration, the reconstruction of social network is also difficult. The family-based urbanization process still has a long way to go.Most of the existing studies on housing and the SWB have focused on overall residents or urban residents, and few researchers have explored the relationship between the housing and the SWB of rural-urban migrants and its internal mechanism. This study contributes to the literature by focusing on rural-urban migrants with empirical evidence.The results of this study also have certain policy implications for solving the housing problem of rural-urban migrants:First, commercial housing, as an important housing asset, has a significant effect on the SWB of rural-urban migrants. Moreover, Chinese residents’ SWB is higher when the local government promises greater dedication to housing affordability improvement . TherefoAlthough the China household finance survey (CHFS) data used in this study is a national large sample survey data, there are still some shortcomings. First, this paper explores the impact of the independent variable of owning commercial housing on the SWB of rural-urban migrants. However, due to the limitations of data and sample size, no detailed analysis has been carried out on how the location and quantity of their commercial housing affect their SWB, and these issues still require more in-depth research; Second, The timing of the purchase of commercial housing by rural-urban migrants varies, therefore, the 2017 data selected for this study can only observe static information on rural-urban migrants’ housing in that year and cannot capture the dynamic comparison of rural-urban migrants’ SWB before and after owning commercial housing, which is likely to lead to biased estimation results. Hence, in future research, if suitable panel data are available, a more rigorous and detailed study can be considered, which is a direction for future improvement.


The robot joint is the foundation of prosthetics and exoskeletons, which is the key to realizing human-computer interaction for diverse motion characteristics. From the perspective of physiological anatomy, human joints show unique structural characteristics. For example, the knee joint provides a compound joint rotation formed by sliding and rotating between the femur and the tibia, thus realizing a unique physiological characteristic of the Instantaneous Center of Rotation (ICR). Therefore, simply using a uniaxial hinge as a knee prosthesis or exoskeleton may cause uncoordinated movement between external devices and the human body. Certainly, these unique joint characteristics can be improved by using multi-bar mechanisms or some combined mechanisms, leading to matched and imitated physiological performance of human joints from the perspective of bionics . HoweverFrom the perspective of actuation, the human body retains a compliant actuating system composed of muscles and bones, which have excellent task adaptability under various movement conditions. Therefore, if a robotic joint is actuated only by a motor-reducer system, it may cause problems such as higher energy consumption and less impact resistance, resulting in compromised adaptation to the complex movement conditions of prosthetics and exoskeletons. Taking an ankle-foot prosthesis or exoskeleton as an example, when the subject suddenly stops or steps in the air for some reason, the large instantaneous force on the sole will be directly reflected on the motor, causing the motor to stall or even burn out. However, in human joints, external shocks and load fluctuations have less impact on the soft muscles. Moreover, this external mechanical energy may also be absorbed and released at the right time and location. Inspired by this phenomenon, compliant actuators such as variable stiffness actuators  and someFrom the control point of robotic joints, the essence is to make the prosthesis and exoskeleton keep up with human motion intentions. This often requires detailed joint control systems modeling or a supervised or unsupervised data-driven approach to collect large amounts of data for model training. Moreover, accurate joint motion detection and human intention recognition are essential for efficient joint control due to the need for closed-loop control. Typically, inertial sensors, load cells, sEMG sensors, EEG sensors, visual approach, and physiological indicators  can be used to percept human motion intentions . HoweverPan et al. proposed a novel robot-aided upper limb rehabilitation system via visual, auditory, and tactile feedback in combination with a game-based training task. Compared with only audio-visual-based rehabilitation approaches, the design in this article can help patients enjoy training, improving their motivation and active participation in the tasks. Assfaw and Seid designed a higher-order sliding mode controller for an MR damper-based semi-active prosthetic knee. The authors first built the dynamics model of the single-axis knee joint based on Lagrangian mechanics and then analyzed the MRF using the Bingham Plastic model. The proposed super twisting sliding mode controller is verified in MATLAB simulations, and the results indicate that the knee profile tracking accuracy is improved and the walking gait is near the natural gait. Bai et al. proposed a bio-inspired hydraulic actuator that could imitate the configuration of the human muscle via a single power source and multiple plunger pistons. Each piston chamber is connected to a two-position three-way switch valve, based on which the actuator can output different forces by recruiting the effective areas of plunger pistons in a fixed order. In addition, a robotic arm was designed and used to demonstrate that the proposed actuator can improve the efficiency of the hydraulic system with variable loads. Ramos et al. compared the mechanical properties of hand exoskeleton textile and silicone actuators in terms of physical dimensions, the air pressure required to achieve a full bending motion, and the forces generated at the tip of the actuator. The results indicate that actuators constructed using pleated textile techniques have greater potential in terms of 78% lighter weight, 72% less actuation pressure requirement, 48% more bending force, and 35% more block force for the construction of hand exoskeleton.After the peer review process, this Research Topic accepted four research articles. In summary, all the articles presented on this Research Topic have provided novel and in-depth understandings of the design, actuation, and control of the prosthesis and exoskeleton, facilitating readers to get a comprehensive understanding and cognition of the current development direction and application prospects of robotic joints.


High-quality development is the theme of China’s economic and social development in the new era, and it is also an objective need for tourism development in the 14th Five-Year Plan period. This study presents an investigation of China’s patterns of tourism environmental efficiency from the perspective of spatiotemporal interactions. A nested analytical framework of quantile regression and spatiotemporal leaps was used to explore the driving mechanism patterns of tourism environmental efficiency under different leap types. Based on various spatial analysis methods, we posit that the patterns of tourism environmental efficiency differ through spatial associations, dynamic evolutions, and transition mechanisms. Our results indicate that there is a dynamic convergence trend of the overall differences in tourism environmental efficiency in China from 2000 to 2020 where a significant clustering phenomenon is observed in space and the level of spatial clustering gradually tends to be stable. In terms of local spatial structures and the dependence directions of tourism environmental efficiency, China’s northwest and northeast regions are more volatile, while eastern coastal regions are relatively stable. Spatiotemporal leaps of tourism environmental efficiency show certain transfer inertia with strong spatial dependence or path-locked characteristics, among which most central and western regions always maintain high carbon emission attributes. These regions are the most limited in the synergy of tourism environmental efficiency. The spatiotemporal network patterns of tourism environmental efficiency are mainly based on positive correlations and show strong spatial integration. However, a few neighboring provinces still have a certain degree of spatiotemporal competition. Driving patterns of the spatiotemporal leaps in tourism environmental efficiency among regions differ greatly. The eastern coastal provinces are driven by population-urbanization constraint patterns, and the northwest, southwest, and northeast regions are driven by technology regulation patterns. From the southeast to the northwest, the leap in the environmental efficiency of China's tourism gradually shows a stepwise pattern of "congruent constraint-reverse development-congruent development.” Therefore, the government should not only consider these various driving/constraining factors but also combine different environmentally-efficient tourism clustering types and transition paths to emphasize differentiated environmental tourism measures. This can help avoid the closure of inter-provincial tourism policies through inter-regional synergy. Since the reform and opening up, China's economy gained a rapid growth of GDP and a series of achievements. But with the increase of the total amount and scale, the original power of economic growth will no longer meet the requirements of high-quality development in the new era. China's economic development has entered a new era, promoting regional integration and improving production efficiency are important support for realizing the transition from high-speed growth to high-quality development. From a broad perspective, the high-quality development of China's tourism industry requires improving the functional benefits of tourism and thus exerting positive external effects. From a narrow perspective, the high-quality development of China's tourism industry requires upgrading the quality of tourism products,improving the efficiency of tourism economy, optimizing the structure of tourism industry and enhancing the sustainability of tourism. Against the backdrop of achieving the "dual carbon goals", promoting green development of the tourism industry has become a dependent path for high-quality development of the tourism industry in the new era. As an important component of the national economic system, the tourism economy usually relies on large-scale investment in resources and energy. The inefficient and high consumption extensive tourism development model undoubtedly has caused many interferences to resources and the environment, leading to increasingly severe ecological and environmental problems. The contradiction between the development of tourism economy, resource allocation, and environmental protection is increasing day by day; the development of high-quality tourism places higher demands on the rational allocation of resources and the improvement of environmental efficiency. Therefore, there is an urgent practical need to study the efficiency of tourism environment.1. Tourism efficiency refers to obtaining the maximum output with the minimal amount of tourism-related resource input within a certain time frame to satisfy stakeholders' needs for maximizing the total surplus. This can reflect the reasonableness of tourism growth2. The study of tourism efficiency is important for the high-quality development of tourism. The high growth of the tourism economy is valued by the academic community to coordinate its development; regional tourism differences and tourism spatial structure have become hot topics of research. In-depth research on the tourism industry, regional tourism development, ecotourism, and other related tourism efficiency topics3 has produced crucial results4. Most relevant studies consider regions with developed tourism such as Europe and the United States as case sites, select comparative factors to construct measurement models, determine regional tourism resource endowment5, tourism competitiveness6, and source markets7 based on static cross-sectional data or dynamic panel data, and describe their spatial differentiation characteristics8, causes9, and impacts12. Research perspectives include measuring tourism scale13, efficiency14, or performance15 from multi-level scale units, such as zones2, provinces16, municipalities17, and counties18, using single indicators such as tourism arrivals, revenue, and related increments or constructing comprehensive evaluation index systems and input–output indicators. Moreover, research methods used include scale distribution theory19, spatial field effect theory20, gravity model21, spatial econometric model22, as well as total factor productivity23, two-stage nested Thayer coefficient decomposition24, and exploratory spatial data analysis25 to explore the characteristics of spatiotemporal differentiation, spatial structure evolution, influencing factors, and driving mechanisms of tourism development28. Specifically, scholars' research on tourism efficiency focused on hotel management performance and operational efficiency. Kim et al.29 analyzed hotel efficiency, including customer satisfaction, and observed that the overall hotel efficiency value could better reflect hotel productivity. Arbelo-Pérez et al.30 used the stochastic frontier model to measure the efficiency of the hotel industry. Higuerey et al.31 used the network data envelope to assess the efficiency of the hotel industry in Ecuador. Subsequent related studies gradually extended to travel agencies and tourist attractions and destinations. Dragan et al.32 proposed an assessment model for travel agency efficiency. Abrate et al.33 used a two-stage data envelopment analysis to explore the operational efficiency of traditional travel agencies. Furthermore, Niavis34 evaluated the comprehensive spatio-temporal efficiency of tourist destinations in the Mediterranean coastal region. Based on a literature search, we found that a number of domestic and foreign scholars have studied tourism efficiency; however, relatively few studies have focused on the topic of tourism environmental efficiency. Environmental efficiency refers to the provision of products or services with competitive price advantages that meet people's pursuit of a happy life, while reducing the impact of products or services on the ecological environment and the intensity of resource consumption. This study considers tourism environmental efficiency as the ratio of input elements, such as tourism resources, labor, tourism capital, and environmental management, to output elements, such as tourism economic value and their impact on the environment in a complex tourism economic, resource, and environmental system. It measures the economic value generated by tourism economic activities and evaluates the impact caused by tourism economic development on the ecological environment. Higher tourism environmental efficiency value is correlated with greater tourism economic value and a lesser environmental load caused by tourism development.Efficiency is the utility ratio between inputs and outputs of economic production factors that requires regional economic development to balance resource utilization and output effects38.Tourism efficiency has been studied systematically by scholars and it has a solid theoretical foundation in research. While many scholars have analyzed the spatial differentiation of tourism environmental efficiency based on geography, most studies have been limited to the cross-sectional characteristics of spatial differences and correlations and so far have not depicted spatial relationships, patterns, or changes from the perspective of spatiotemporal interactions. In terms of revealing the influencing factors, existing studies have mostly examined and analyzed the influencing factors of tourism environmental efficiency with the help of traditional decomposition methods or regression models. However, these tend to ignore the heterogeneity of driving factors at different levels of tourism environmental efficiencyI index, exploratory spatio-temporal data analysis, Quantile regression and other methods were used. We selected 2000–2020 as the study period. The indicators' data were obtained from the China Statistical Yearbook, China Environmental Statistical Yearbook, the China Tourism Statistical Yearbook, statistical yearbooks of provinces , and the official websites of provincial cultural and tourism departments between 2001 and 2021. A nested analytical framework of quantile regression and spatiotemporal leaps was used to explore the driving mechanism patterns of tourism environmental efficiency under different leap types. On the one hand, it is intended to improve the macroscopic cognition of the current situation of tourism environmental efficiency and its dynamic change patterns as well as reveal the driving modes of leap changes in tourism environmental efficiency under the influence of multiple factors. On the other hand, it will help to fully explore the potential of tourism environmental efficiency in practice, and provide a reference for the formulation of "common but differentiated" tourism environmental policies based on the differences in the effects of various driving modes under different leap types. Additionally, it may help to fully explore the potential of tourism environmental efficiency and provide a reference basis for the formulation of "common but differentiated" tourism environmental policies based on the differences in the effects of various driving models under different types of transition. This can serve the practical needs of constructing low-carbon tourism under a "double carbon" objective.This study investigates the spatiotemporal evolution and dynamic interaction characteristics of tourism environmental efficiency in China from 2000 to 2020 through the integrated application of multiple spatial analysis methods. Moran's I index in the spatial autocorrelation analysis method was used to analyze the spatial correlation and degree of agglomeration of tourism environmental efficiency in each province of China. The value range of Moran's I index is , and when the index value is 0, it indicates that there is no spatial correlation. When the value is closer to 1, it indicates a stronger positive spatial correlation between regional units and a higher degree of agglomeration. The specific formula is as follows:n is the number of study units (provinces), xi and xj are the tourism environmental efficiency values of neighboring provinces, wij denotes the spatial weight matrix.There are many indicators and methods to measure spatial autocorrelation. Moran's I index is one of the most commonly used basic indicators, which can comprehensively characterize the average spatial correlation, spatial distribution pattern and significance of specific variables or attributes of each unit in the region. Therefore, the global Moran's 39. The ESTDA method can effectively integrate time and space, which helps compensate for the lack of measurement in traditional spatial analysis methods in the time dimension and realizes spatiotemporal interaction analysis40.Spatiotemporal interaction is an important method and perspective for geographers to study socio-economic development changes; however, previous studies on the spatial variation of geographic variables and their evolution have focused more on measuring cross-sectional characteristics41. Specifically, two indicators, the relative length, and curvature were included42.U is the relative length; β is the curvature; N is the number of study units; d is the distance of study unit i moving during the period from t to t + 1. If U and β are greater than 1, it indicates that the study unit has a more dynamic local spatial structure and dependence direction during the study period, and vice versa.The LISA time path reflects the geometric characteristics of the movement of geographic variables in LISA coordinates in the Moran scatter plot. The analysis of the pairwise migration of attribute values and spatially lagged values of tourism environmental efficiency in each province explains the characteristics of spatiotemporal synergistic changes and the dynamics of regional tourism environmental efficiency43 and combines LISA temporal paths with traditional Markov chains to form a spatiotemporal leap matrix, which mainly includes four types: type I (only self-leap), type II (only domain leap), type III (both self and neighborhood leap), and type IV (both self and neighborhood remain stable)  Table . Based o44 to compensate for the loss of certain insignificant correlation information in traditional spatial analysis. This study calculates the covariance coefficients of the LISA spatiotemporal movement trajectories between each province and neighboring provinces and visualizes them in the form of a spatiotemporal topological network to reveal the degree of similarity in the competing dynamics and development mechanisms between provinces during the dynamic evolution of tourism environmental efficiency45.The spatiotemporal interaction characteristics of socioeconomic development changes can also be characterized by introducing graph theoryBased on the covariance coefficients of LISA spatiotemporal trajectories, the dynamic correlations among neighboring provinces can be classified into four types: strong positive correlations (0.5 ~ 1), weak positive correlations (0 ~ 0.5), strong negative correlations (− 1 ~ − 0.5) and weak negative correlations (− 0.5 ~ 0). When the value of the covariance coefficient is greater than 0, it is a positive association state, indicating that neighboring provinces are in a positive synergistic development state in the process of spatiotemporal change; when the value is less than 0, it is a negative association state, indicating that there is a certain degree of spatiotemporal competition between neighboring provinces; and when the value is 0, there is no dynamic association relationship between neighboring provinces.46, and the estimation is more robust. At the same time, the model portrays the conditional distribution more carefully and can be deeply nested with different types of spatiotemporal leaps47, which helps resolve and discriminate the driving mechanisms of spatiotemporal leaps in tourism environmental efficiency at different quantile levels. The details are as follows:Unlike traditional OLS estimation, quantile regression is used to estimate the parameters by minimizing the sum of the absolute values of the weighted residuals. Thus, it is less susceptible to the constraints of the distribution assumptions and outliers Y is:Assume that the probability distribution of variableτ is defined as:The quantile of q(τ) of F(y) is:Then the minimum objective function of quantile 48 and Patterson et al.49, tourism transportation, tourism accommodation, and tourism activities are identified as the key areas of CO2 emissions in the tourism industry. The decomposition and aggregation method is adopted to measure the CO2 emissions in the tourism industry from the bottom up. The specific calculation method is as follows:Tourism environmental efficiency is a comprehensive reflection of the coordinated development of tourism and environment systems. Tourism environmental efficiency should comprehensively measure the input and output elements of tourism and the environment. Based on the principles of importance, comparability, scientificity, accessibility of the selected indicators, and previous studies, the input–output evaluation index system of tourism environmental efficiency was constructed Table . Among tCt represents the total CO2 emissions (g) from the tourism industry in year t; 2 emissions (g) of sector j in year t; 2 emissions from tourism transportation in year t (g); 2 emissions (g) from tourism accommodation in year t; 2 emissions (g) from tourism activities in year t.In the formula, 50, and the level of economic development (E), industrial support capacity (I), urbanization level (U), technology development (T), transportation accessibility (TA), openness degree (O), environmental regulation level (ER), environmental self-cleaning capacity (ES), the relative abundance of tourism resources (TR), and human capital (H) are selected as the ten indicators used as detection factors , and the official websites of provincial cultural and tourism departments between 2001 and 2021. Missing data were obtained using the Linear interpolation method. The basic geographic data were mainly from the 1:4 million databases of the National Geographic Information Center. Individual missing data were supplemented by interpolation, and the relevant economic data were uniformly converted to constant 2000 prices. In addition, the variables were normalized before the empirical analysis to reduce heteroscedasticity and nonstationarity.Based on DEASOLVER Pro 5.0 software, the non-radial (non-oriented), variable scale payoff (VRS) super-efficient SBM model was used to measure the tourism environmental efficiency of 31 provinces and cities in China in 2000–2020, estimate the average yearly value to conduct a comparative analysis of different regional tourism environmental efficiencies. The mean efficiency values were compared and analyzed ,,2), whicAs shown in Fig. As shown in Fig. t → HHt + 1 and HLt → HLt + 1, indicating that the high carbon emission attributes are very stable in most of the central and western regions, which are the key regions limiting the synergy of tourism environmental efficiency. Overall, the tourism environmental efficiency of most Chinese provinces is less affected by the spatial influence of the domain, and its factors have a stronger influence on the spatial correlation structure, with strong path-dependent or spatially locked features.The spatiotemporal leap matrix can be used to further analyze the mutual transfer status of local spatial association types. As shown in Table In the process of economic development and tourism, there may be spatiotemporal competition or a cooperation-win–win relationship between neighboring provinces. This study further explored the spatial association characteristics between neighboring provinces using a spatiotemporal topological network diagram. The spatiotemporal topological relationship is the foundation for defining new spatial objects, providing a feasible explanation for describing the relationships between new spatial objects and other spatial objects. It also involves the transformation and deformation of objects, as well as the connections between objects. It plays a crucial role in Geographic Information Systems (GIS) as it can define the position and properties of spatial objects in spatial structures. As shown in Fig. E), industrial support capacity (I), urbanization level (U), science and technology development level (T), transportation accessibility (TA), openness to the outside world (O), environmental regulation level (ER), environmental self-cleaning capacity (ES), tourism resource abundance (TR), and human capital (H) were included in the quantile regression model, and drawing on related studies52, a total of five quartiles of 0.1, 0.25, 0.5, 0.75, and 0.9 were selected for estimation. To reduce pseudo-regression, a smoothness test was required before the empirical analysis. In this paper, the LLC and Fisher-ADF unit root tests were selected to test the smoothness of each variable  and low quantile (0.1 ~ 0.5), while four quantile response types, high quantile constraint, high quantile drive, low quantile constraint, and low quantile drive, can be further constructed according to the direction of differential effects of influencing factors, which can then be deeply nested with the spatiotemporal leap types53 Table . The chaOn the basis of the above analysis, the influencing factors of the high and low quantiles and the type of space–time transition are deeply nested, and four driving/restricting modes of space–time transition of tourism environmental efficiency are established Fig. , which aEnvironmental protection and tourism resource abundance are the main factors that inhibit the growth of provinces with low tourism environmental efficiency, and the type of low-quantile constraints belong to this type of model. The environmental protection generally has a two-way impact on the tourism environmental efficiency. On one hand, environmental self-cleaning capacity can reduce the marginal abatement cost through the scale effect; on the other hand, an elevated level of environmental regulation may lead to diseconomies of agglomeration, and overcrowding causing all kinds of competitive costs to rise, thus generating excessive energy demand. In addition, environmental regulation also shows a positive driving effect on tourism environmental efficiency but fails to pass the significance test. The reasons may be that, first, environmental regulation, as a constraining force, increases the cost of pollution control for tourism enterprises, which may further crowd out their R&D investments and hinder energy efficiency and technological innovation; second, energy consumption in tourism in high quantile regions has increased significantly during the study period and still shows growth, resulting in the environmental regulation effect not yet fully manifested; third, it may be due to the government; third, it may be due to the lack of effective government regulation and performance control in the process of environmental governance, resulting in relevant regulatory instruments that have not yet formed effective constraints. Combined with the previous analysis Tables  and 8, iThe degree of openness to the outside world and accessibility are the main factors driving the leap to low-value agglomerations in tourism environmental efficiency; the low-quantile drive belongs to this model. In addition, the degree of openness to the outside world is significant in promoting the efficiency of the tourism environment. Accelerating the opening-up process and transportation accessibility can promote the agglomeration of talent and technologies in the service industry, and accelerate knowledge spillover and innovation resource sharing among service enterprises, all of which can help enhance tourism environmental efficiency. During the study period, China's tourism industry as a whole was in the scale expansion stage, and the industrial structure was still dominated by traditional high-energy-consuming low-end tourism, while the internal structure optimization and adjustment of the tourism industry were slow, and the "high technology" and "high knowledge-intensity" of the modern tourism industry The advantages of "high technology" and "high knowledge intensity" of the modern tourism industry have not yet played a significant role in constraining the efficiency of the tourism environment. Driven by the effect of this model, the original low-value area of tourism environmental efficiency gradually begins to leap upward, and only Hunan and Guizhou belong to this type, which is much smaller than the number of provinces in the low-quantile constraint type, indicating that the environmental resource constraint model has a broader influence in the low-quantile stage.High-quantile constraints are applied to the model. In this model, the industry support capacity and urbanization level still maintained a significant inhibitory effect on provinces with highly tourism environmental efficiency, and the constraint effect gradually increased as the quantile point increased. At the same time, the level of tourism economic development and its quadratic term both pass the significance test at the high quantile stage and show a "positive–negative" effect, indicating a non-linear relationship between tourism value added per capita and tourism environmental efficiency, which verifies to a certain extent that the development of service economy supports The "environmental Kuznets curve" hypothesis. Before the economic development level reaches the inflection point, tourism environmental efficiency increases with an increase in tourism value-added per capita. At this stage, the tourism industry shows a rough-scale expansion. When the added value of tourism per capita reaches a certain level, the growth mode of the tourism industry starts to become intensive, positive externalities such as economies of scale and knowledge spillover effects continue to emerge, emissions reduction efficiency increases and tourism environmental efficiency is further constrained. In addition, the level of urbanization had the same negative inhibitory effect on tourism environmental efficiency, passing the significance test at the 1% level. With the acceleration of the urbanization process and the promotion of intensive production and lifestyles, energy constraints and environmental pollution problems have become increasingly prominent, further inhibiting the improvement of tourism environmental efficiency. Combined with the previous analysis, Jiangxi, Henan, Shandong, Guangdong, Guangxi, Inner Mongolia, Heilongjiang, Jilin, Liaoning, Xinjiang, Qinghai, Tibet, Yunnan, Ningxia are significantly constrained by the effect of this model and gradually leap from the original high-tourism environmental efficiency area to the low-value area. Among them, Jiangxi, Henan, Shandong, Guangdong, Guangxi belong to the same direction constraint type, and the growth of tourism environmental efficiency in neighboring provinces is also hindered. Inner Mongolia, Heilongjiang, Jilin, Liaoning, Xinjiang, Qinghai, Tibet, Yunnan, Ningxia belong to the reverse development area, and the tourism environmental efficiency of neighboring provinces continues to grow without being affected by the inhibiting effect.A high-quantile drive is applied to this model. Technological development has both positive and negative effects on energy consumption. From the empirical results of this paper, the level of technological development has a positive driving effect on tourism environmental efficiency. This may be because, on the one hand, changes in the level of technological development stimulate the economic activities of the tourism industry, which may induce new energy consumption and demand, making it difficult to form economies of scale in energy consumption. On the other hand, there may be time lags in the application and diffusion process of the level of technological development, and its emission reduction effect has not yet fully emerged. Among them, most of the central and western provinces are more influenced by this driving model, especially the western region, which shows an obvious homogeneous development trend. Environmental efficiency increases at the same time, driving the growth of tourism environmental efficiency in neighboring provinces, Hainan, Hubei, Gansu, and other places belonging to this type. While Sichuan, Chongqing, Shaanxi, Shanxi, and other places belong to the reverse development area, the neighboring provinces’ tourism environmental efficiency declines, and an emission reduction gap gradually emerges.In general, from the southeast to the northwest, the leap pattern of China's tourism environmental efficiency gradually shows a stepwise progression of "congruent constraint—reverse development-congruent development" Fig. . Among tThis study examined tourism environmental efficiency patterns of Chinese provinces from 2000 to 2020 by using spatial analysis methods to analyze the differential patterns and spatiotemporally dependent dynamic evolutions. We aimed to further explore the factor-driven patterns of the leaps in tourism environmental efficiency through the nesting of spatiotemporal leaps and quantile regressions. Based on our findings, the following conclusions were drawn:I indices are all positive, indicating that inter-provincial tourism environmental efficiency has a positive spatial correlation and presents a spatial clustering phenomenon in spatial distribution.First, the mean value of tourism environmental efficiency in China increased and then fluctuated during the study period, showing overall spatial non-equilibrium. The results of the kernel density estimation indicate that the overall difference in tourism environmental efficiency is narrowing; however, a few high carbon emission regions show a polarization effect and the gap with other regions is increasing. In addition, the spatial autocorrelation Moran's Second, the analysis of the spatiotemporal dynamic process based on the ESTDA shows that the overall spatial association pattern of tourism environmental efficiency in China during the study period shows transfer inertia with strong path dependence or spatial locking characteristics. The eastern coastal region has a relatively stable local spatial structure and spatial dependence, whereas the central and western regions show the opposite. In addition, there are uneven characteristics between neighboring provinces in the process of spatial and temporal changes in tourism environmental efficiency, such as Tianjin, Hebei, Yunnan, Guizhou, Qinghai and Tibet. There is a certain degree of spatial and temporal competition and thus the development of low-carbon actions in tourism must give full play to the synergistic effects between regions and avoid the closure of interprovincial tourism environmental policies.Third, the factor-driven/constrained patterns of the spatiotemporal leap in tourism environmental efficiency can be divided into four categories: openness-transportation driven, environment-resource constrained, technology-human driven, and economic-industry-urbanization constrained. The eastern coastal provinces are mainly influenced by the population-urbanization constraint model, whereas most regions in the northwest, southwest, and northeast tend to be influenced by a technology-regulation-driven model. From the northwest to the southeast, the leap in the efficiency of China's tourism environment gradually shows a stepwise pattern of "congruent constraint—reverse development—congruent development."Fourth, environmental regulations also have a positive driving effect on carbon emissions from the tourism industry, but have not passed the significance test. The reason may be that, firstly, tourism environmental regulations, as a restraining force, will increase the pollution control costs of tourism enterprises, which may further squeeze out the R&D investment of tourism enterprises and hinder energy-saving technology innovation; Secondly, the energy consumption of the tourism industry in high-ranking areas has significantly increased during the research period and is still showing a growth trend, resulting in insufficient reflection of the regulatory effect of the tourism environment; Thirdly, it may be due to the lack of effective supervision and performance control by the government in the process of tourism environmental governance, leading to the lack of effective constraints in relevant regulatory measures.Due to the disparity in economic development among provinces and regions, there is an obvious spatial heterogeneity in China's tourism environmental efficiency. Therefore, it is especially crucial to carry out low-carbon actions for provinces with different tourism environmental efficiency levels according to local conditions. Through an in-depth study of the spatial and temporal interaction characteristics and evolution of different patterns of tourism environmental efficiency in China, this study can better clarify the developmental stages and dynamic evolution of tourism environmental efficiency. This study also combines the deep nesting of quantile regression and spatiotemporal leaps to further clarify the differences in the implementation effects of emission reduction measures at different levels of tourism environmental efficiency, effectively avoiding the idealistic treatment mode of mean regression, better reflecting the real situation, and providing a reference for the formulation of "common but differentiated" tourism carbon reduction policies. It also provides a reference for the formulation of common, but differentiated policies for carbon reduction in tourism.The policy insights from this study are as follows. For low-quantile constraint type areas such as Beijing, Tianjin and Hebei, which have strong spatial stability, the focus should be on the improvement and strengthening of the inhibiting factors of tourism environmental efficiency (such as population density and urbanization level). Specifically, reasonable population policies should be formulated based on regional differences, giving full play to the scale and agglomeration effects brought about by the optimization of population density, focusing on the construction of new urban areas, realizing the intensive use of public resources and service facilities, and further improving the allocation efficiency of energy resources to maintain the stability of low-carbon agglomeration. For Hainan, Shaanxi, Shanxi, and other high-quantile driven areas, it is necessary to focus on controlling and optimizing the positive drivers of tourism environmental efficiency. The service industry in these regions should focus on promoting the conversion and upgrading of traditional energy utilization technologies, vigorously developing low-carbon technologies with "green bias,” and accelerating the application and promotion of relevant low-carbon technologies to break the time lag hindrance in technology application. For high-quantile constraint type areas such as Jiangxi, Henan, Shandong, Guangdong, Guangxi, based on the nonlinear relationship between economic development and tourism environmental efficiency, we can accelerate the level of the service economy to cross the inflection point and decouple economic development from carbon emissions through the two-wheel drive of the government and market. For Hunan and Guizhou, and other low-quantile driven areas, in addition to implementing the above low-carbon technical measures, we should continue to deepen the structural reform on the supply side of the tourism industry, take the "new development concept" and "high-quality development" as the guide, prioritize the support of low-energy, low-pollution emerging We should also continue to deepen the structural reform on the supply side of the tourism industry and give priority to supporting new tourism industries with low energy consumption and low pollution, and accelerate the adjustment and optimization of the internal structure of the tourism industry. In addition, each region should actively help the tourism industry change from "double control" of energy consumption to "double control" of carbon emissions, gradually establish a mechanism for the decomposition of tourism targets, and at the same time, adjust the intensity of environmental regulations according to local conditions, and increase the policy of energy-saving and technological innovation for service enterprises. Simultaneously, the intensity of environmental regulations should be adjusted according to local conditions, and policy support for energy-saving and technological innovation in service enterprises should be increased to further reduce the cost burden of environmental regulations. In general, the low-carbon development of tourism in each region should not only consider all kinds of drivers/constraints but also combine different types of drivers/constraints and transition paths, emphasizing differentiated measures to reduce emissions in tourism and avoiding the closure of inter-provincial emission reduction policies by collaborative emission reduction between regions. At the same time, the policy implications contained in this study include: firstly, establishing a regional tourism linkage mechanism to promote the flow of tourism talents, funds, and technology, forming a pattern of regional coordination, industrial integration and complementarity, and resource co construction and sharing, further narrowing regional differences in the tourism industry, and promoting the rational agglomeration of the tourism industry in space and balanced development between regions. Secondly, by optimizing the industrial structure, we should further improve the level of tourism agglomeration and fully leverage the emission reduction effect of industrial agglomeration; Introduce energy utilization technologies and learn advanced experience in energy conservation and emission reduction in high concentration low emission zones, focus on improving energy efficiency, and continuously reduce the carbon emission intensity of the tourism industry. Thirdly, creating a favorable external environment for industrial energy conservation and emission reduction by reasonably guiding the agglomeration of regional tourism industry; we should also transform the extensive growth mode of the tourism economy, increase environmental regulations and opening-up efforts, in order to promote the intensive and low-carbon development of the tourism industry.Due to data limitations, this study only examines changes in tourism environmental efficiency in China from 2000 to 2020. Further enrichment of sample data is needed to explore the spatial and temporal dynamic process of tourism environmental efficiency and its mechanism of action in more depth. In addition, a geographic probe model can be introduced to examine and analyze the interaction effects between different factors affecting tourism environmental efficiency to enrich and improve the influence mechanism of tourism environmental efficiency. Future research needs to integrate many factors of tourism environmental efficiency and construct a more comprehensive impact system. It can be attempted to conduct in-depth research using specific tourism destinations as classic cases to reveal their driving mechanisms from a more microscopic perspective. In terms of refinement of research content, it is necessary to further explore the dynamic correlation and spatial spillover effects of tourism environmental efficiency at different spatial scales in the future.


Aiming to solve the problem of color distortion and loss of detail information in most dehazing algorithms, an end-to-end image dehazing network based on multi-scale feature enhancement is proposed. Firstly, the feature extraction enhancement module is used to capture the detailed information of hazy images and expand the receptive field. Secondly, the channel attention mechanism and pixel attention mechanism of the feature fusion enhancement module are used to dynamically adjust the weights of different channels and pixels. Thirdly, the context enhancement module is used to enhance the context semantic information, suppress redundant information, and obtain the haze density image with higher detail. Finally, our method removes haze, preserves image color, and ensures image details. The proposed method achieved a PSNR score of 33.74, SSIM scores of 0.9843 and LPIPS distance of 0.0040 on the SOTS-outdoor dataset. Compared with representative dehazing methods, it demonstrates better dehazing performance and proves the advantages of the proposed method on synthetic hazy images. Combined with dehazing experiments on real hazy images, the results show that our method can effectively improve dehazing performance while preserving more image details and achieving color fidelity. With the rapid development of modern industrialization and urbanization, haze has become a common natural phenomenon. Haze is composed of a large number of tiny particles such as water droplets and sulfur dioxide. When optical imaging equipment is collecting images, due to the scattering effect of these tiny particles, the contrast of the collected images is low, and many important details are lost. It is difficult to meet the requirements of object detection ,2, targeSingle-image dehazing is a challenging problem, and many researchers have started to study single-image dehazing techniques ,6,7,8. TThese studies are based on physical models of atmospheric scattering and aim to restore images with prior knowledge. However, the optimal choice of prior knowledge is still unclear. In addition, the extent to which these priors obey the image statistics and how they influence the dehazing performance also remain unknown . RecentlBased on the above analysis, in order to avoid dehazing problems such as color distortion and incomplete dehazing, and to preserve as much image detail information as possible, an end-to-end single image dehazing network with multi-scale attention feature enhancement is proposed. This method can directly restore the input haze image to a clear image without estimating the parameters of the atmospheric scattering model.(1)We propose an end-to-end Multi-Scale Attention Feature Enhancement Networks for Single Image Dehazing (MAFE). This method has achieved excellent performance in image dehazing. It can adaptively focus on the high-frequency information of the hazy image and retain more detailed information. Since it does not need to rely on the atmospheric scattering model, it is not affected by the estimated atmospheric light value. Additionally, color distortion is avoided.(2)We propose an attention feature enhancement module, which can adaptively focus on high-frequency information of hazy images, enhance the relevance of contextual information, suppress redundant information, and compensate for the loss of detailed information.(3)We propose a multi-scale attention enhancement module that builds upon the attention feature enhancement module and incorporates a spatial pyramid of dilated convolutions to fully extract and utilize the multi-scale features of the image. This module expands the receptive field and improves the quality of the dehazed image while preserving more detailed information.(4)The experimental results on both synthetic and real-world hazy images demonstrate that our proposed method achieves state-of-the-art single image dehazing methods in terms of dehazing performance. It can well preserve details such as color and texture of the image.The main contributions of this work are summarized as follows:Single image dehazing is an ill-posed problem. At present, the methods for single image dehazing are mainly divided into two categories, one is to artificially summarize the statistical difference between the blurred image and the unblurred image as an empirical prior, and the other is to directly or indirectly learn the mapping function from large-scale datasets of hazy and clear images. Researchers generally refer to the former as prior-based methods and the latter as learning-based methods.The prior-based methods estimate the transmission rate and global atmospheric light based on some prior knowledge of the clean image. They usually rely on the atmospheric scattering model and handcrafted priors. He et al.  found thIn recent years, with the rapid development of deep learning ,21,22,23In this section, we first describe the haze density image prediction model and propose a haze density image prediction network based on multi-scale attention feature enhancement. Then, we introduce the dehazing fusion block (DFB), multi-scale attention module (MAE), attention feature enhancement module (AFE) and context enhancement module (CEM) in detail. Finally, we provide loss functions for training the network.Since it is difficult to accurately estimate the atmospheric light value A in the atmospheric scattering model, HDP-net  redefineAccording to Equation (4), it is only necessary to build a haze density image prediction network The multi-scale attention feature enhancement dehazing network (MAFE) we propose is shown in The FFEM mainly consists of channel attention (CA), pixel attention (PA) , contextThe input of MAFE is the hazy image of the RGB channel. After it is transmitted to FEEM, the convolution kernel is used to extract the shallow information of the hazy image and passed to DFB. DFB focuses on haze and high-frequency information. This network connects three DFBs in series. As the network gradually deepens, a large amount of detailed information will be lost. Therefore, the features extracted by the three DFBs are connected in the channel direction to realize the fusion of deep and shallow information and preserve more details while retaining rich features. The fusion information obtained from FEEM is transferred to FFEM. First, CA and PA are used to further flexibly adjust the obtained fusion haze density information. Secondly, CEM is used to enhance context information and suppress redundant information. Then, the haze density image DFB consists of multilevel attention enhancement module (MAE), convolutional layers and skip connections, as shown in Image features of different scales have different semantic information, so fully extracting and utilizing multi-scale features can effectively improve the quality of dehazing images. Compared with using different ordinary convolution kernels to adjust the size of the receptive field, dilated convolution can achieve the same performance without introducing other calculations and parameters. Mehta et al.  proposedThe input feature map passes through a local residual block in MAE and is then transmitted in parallel to the AFE module and the SPDC. The feature maps obtained from the two paths are concatenated and output. The multi-scale feature information collected by SPDC is a further supplement to the information obtained by the AFE module, which reduces the loss of haze information and high-frequency information during the transmission of the data stream through the AFE module, and eliminates grid artifacts. The MAE module mathematical model is shown in formula (6).In order to make up for the loss of some details in the existing attention module during image processing, we propose the AFE module, which is mainly composed of a local residual block, CA, PA and CEM, and the structure is shown in Our improved CA is shown in Secondly, in order to focus on more image details, two convolutional layers and a Rule activation function are used to fuse the channel features on the two paths. The fusion function is expressed as:Then, we use the sigmoid function to assign the weights of different channels, the expression is as Equation (11).Finally, the input feature map PA module can pay attention to thick-hazed pixels and high-frequency image region. As is shown in Although PA can focus on high-frequency information, the receptive field of PA in shallow features is limited, making it difficult to ensure that subsequent feature learning has sufficient context information. In order to capture more context information, we use CEM based on the enhanced attention mechanism, as is shown To normalize the learning direction of our proposed network during training, we use the combination of The In image restoration tasks, perceptual loss is widely applied to the perceptual difference between two images, which can obtain extremely rich, detailed information. In this paper, VGG-16  is used In this section, first we introduce the dataset and experimental details. The performance of our proposed method is then evaluated on synthetic and real-world datasets and compared with state-of-the-art dehazing methods. Finally, the effectiveness of our proposed module is demonstrated through ablation studies.We conducted experiments on the publicly available Realistic Single Image Dehazing(RESIDE) dataset . The datWe employed the Pytorch framework with NVIDIA RTX8000 GPU on both training and testing stages. The proposed network was trained in RGB channels and augment the training dataset randomly rotated by 90, 180, 270 degrees and horizontal flip. Images were resized to 240 × 240 through preprocessing, and the ADAM optimizer was implemented with a batch size of 4. The whole network is trained for The initial learning rate is set to 0.0001, we adopt the cosine annealing strategy  to adjusWe train continuously and plot the learning curves by obtaining PSNR and SSIM scores every 5000 steps, as shown in In this section, we compare our proposed architecture with the following state-of-the-art dehazing methods: DCP , CAP 1717, AOD-NWe use Peak Signal-to-Noise Ratio (PSNR), Structural Similarity (SSIM) and Learned Perceptual Image Patch Similarity (LPIPS) among image quality assessment methods to evaluate the dehazing performance of other state-of-the-art methods. The objective results of various dehazing methods on the synthetic dataset are shown in The subjective results of various dehazing methods on the SOTS-outdoor test set are shown in To verify the dehazing performance of proposed method on real hazy images, we conducted tests on the NHHAZE and RTTS datasets, respectively. The subjective results on the NHHAZE dataset are shown in We also performed quantitative evaluations on the real hazy images in NHHAZE, as shown in The subjective results of various dehazing methods on the RTTS dataset are shown in The subjective results of various dehazing methods in the real world are shown in To demonstrate the effectiveness of our proposed method, we conducted an ablation study to analyze the MAE and AFE modules. We cropped the image to First, we constructed a baseline network without the SPDC and CEM moules, represented as “Baseline” in (1)When our proposed method does not include the SPDC and CEM modules, that is, the baseline network, the dehazing results are the worst.(2)When the SPDC module is added to the Baseline network, compared with the dehazing results of the Baseline network, the PSNR scores is increased by 2.26, the SSIM scores is increased by 0.0152, and the LPIPS distance decreased by 0.0028, which proves the effectiveness of the SPDC module in improving the dehazing performance.(3)When adding the CEM module on the baseline network, that is, the AFE module we proposed, compared with the dehazing results of the baseline network, the PSNR scores is increased by 2.48, the SSIM scores is increased by 0.0184, and the LPIPS distance decreased by 0.0028, which proves the dehazing performance of the proposed AFE module.(4)When both SPDC module and CEM module are added to baseline network, that is, the MAE module proposed in this paper, the network model is our proposed method. The PSNR and SSIM scores are the highest and the LPIPS distance is the shortest in According to the quantitative results of the ablation study on the SOTS-outdoor dataset in We developed a new image dehazing network model that demonstrated good dehazing performance. The MAE module and AFE module we proposed use the attention mechanism to adaptively extract haze features, the CEM module enhances contextual information, suppresses redundant information, and dilated convolution expands the receptive field, which can greatly compensate for the loss of detailed information. Therefore, compared with several other state-of-the-art dehazing algorithms such as AOD-net and FFA-net, our network can better capture the color, texture, and other detailed information of the image. Networks such as AOD-net optimize their network models by calculating mean square error (MSE). Our network is the same as FFA-net, using both PSNR and SSIM to optimize the network model. Therefore, our network can directly obtain the best network model corresponding to the maximum PSNR score and SSIM score. In addition, our proposed MAE module and AFE module are universal, which means that they can be easily plugged into network models in other fields. For example, in the field of medical imaging , they caIn this paper, we propose an end-to-end multi-scale attention feature enhancement network for single image dehazing, which can well preserve image color, texture, and other detailed information. Around the goal of retaining more detailed information of images, we designed the Attention Feature Enhancement module and the Multi-Scale Attention Enhancement module, which focuses on high-frequency information and haze information. Our proposed network was tested on synthetic and real haze datasets for both qualitative and quantitative evaluation. Experimental results have shown that the proposed method has achieved state-of-the-art results. Through ablation studies, we studied the effectiveness of the different modules proposed.


In the last few years, interest in wearable technology for physiological signal monitoring is rapidly growing, especially during and after the COVID-19 pandemic ,3. SpeciEven if technology has successfully increased the comfort of PPG sensors, in terms of wearability, dimensions and battery life, scientific research is still working on several issues, e.g., poor sensor contact, which leads to acquiring signals corrupted by noise and motion artifacts, especially during physical activity . In thisThis Special Issue focused on original research papers dealing with hardware and software advances in the development of robust and reliable biomarkers for the non-invasive monitoring of cardiovascular dynamics based on PPG signal acquisition. Topics of interest for PPG signal applications included clinical pathologies, biometry, sleep and sport monitoring.In the current Special Issue, the optimization of hardware and software systems for a reliable PPG signal acquisition and pre-processing was one of the main topics. The influence of the light source wavelength of the PPG sensor on the accuracy of blood pressure estimation was investigated by Toda et al. , who proAmong the growing plethora of wearable PPG sensor applications, clinical studies certainly play a leading role, considering the enormous recent advances in the field of telemedicine and e-health. In this Special Issue, the advantages of using the PPG signal were explored in several medical fields. PPG sensors integrated into wearable systems for light reflection rheography (LRR) could play a role in the early detection of deep vein thrombosis (DVT) in the lower limbs, as described in . In thisGiven the growing use of wearable systems to monitor physiological signals during sleep, our Special Issue could not miss the contributions regarding this crucial aspect that significantly influences an individual’s quality of life. Lagazzera et al. presented UpNEA, a novel sleep-monitoring platform based on a smart glove, recording PPG, SpO2, and three accelerometer signals, a mobile application and a remote server . The macOne of the most relevant advantages of wearable PPG systems is that thanks to their absolute non-invasiveness, they can be used during sports activities to monitor physical effort and prevent life-threatening situations. Although further testing is needed, an algorithm for the real-time processing of pulse oximeter and accelerometer data and recognition of pre-drowning symptoms in swimming pools was proposed in .PPG signals are also currently used for biometric purposes, given the possibility of acquiring them easily and at low cost. A novel algorithm based on the study of PPG signal diffusive dynamics was reported in , and it As part of this Special Issue, new frontiers regarding PPG signal acquisition in contactless mode have been explored in the work of van Es et al. . The autIn conclusion, the contributions to this Special Issue allowed a varied journey into novel algorithms for PPG series processing and data analysis, new solutions for signal acquisition in both wearable and contactless modes, and interesting fields of application from clinical to sport and biometric monitoring.All the findings reported reaffirm the need to continue refining existing techniques and proposing new approaches for the non-invasive acquisition and processing of PPG signals, which with its many strengths can continue to revolutionize our daily lives and clinical practices aimed at diagnosis and prevention.