Impact of urban-rural development and its industrial elements on regional economic growth: An analysis based on provincial panel data in China.

Urban-rural development is an important driving force for regional economic growth. The existing researches have studied this issue from various perspectives, but they ignore the impact of big data on the economy. In the post pandemic era, big data, as an emerging production factor, has a significant indicative effect in promoting urban-rural economic recovery and fostering new business forms. Therefore, fully considering the factor of big data can help reveal its impact mechanism on urban-rural economic growth in the post-epidemic period. Based on the data of 30 provinces and cities in China, this paper introduced big data on the basis of traditional models and constructed a multi-dimensional factor indicator system. At the same time, the panel regression model was established by using unit root test, Hausman test and precision test. Through benchmark regression and heterogeneity analysis, the impact of urban-rural development factors on economic growth was discussed. The results showed that the panel model passed all tests, and its regression error was stable below 5 %. Transportation, technology, and the three major industries can all promote positive economic growth, with a significance of 1 %. The three industries' contribution to economic growth ranks the third, second and first industries in order. In addition, the good ecological environment contributes to the benign economic growth during the study period. A 1 % increase in forest cover would drive economic growth by 0.215 %. But the impact of public's attention on the overall economy was an indirect effect manifested through its physical industries.The regional heterogeneity indicated that each element had different effects on economic development in eastern, central and western regions. Based on its results, this paper proposed suggestions for each region. In addition, this study found that the Internet attention reflected by big data did not directly drive economic growth, but affected economic growth through indirect channels such as information flow and resource allocation of real industries. This study provided data support for the existing theoretical review, and provided policy reference for the rational planning and industrial layout of China's regional economy.

Modeling method of local financial dependence: Evidence from Mongolia.

Many countries have been striving to equalize the balance between the central government and sub-government financial disparities without considering political interference. This paper aims to summarize the theory of local financial federalism and propose an unprecedented model for identifying the factors that affect local financial dependence. The proposed model is based on a theoretical framework incorporating five assumptions and is applied through panel regression analysis. Utilizing panel data from 2013 to 2022 in 21 provinces of Mongolia, a total of nine variables have been identified and econometrically tested within the proposed model. The findings from the panel regression analysis reveal that local budget investment, local personal income tax, local property tax, and other local taxes positively impact the reduction of local financial dependence. However, it is observed that an increase in local budget expenditures and GDP leads to an escalation in local financial dependence.

Impact of education and income inequalities on life expectancy: insights from the new EU members.

Life expectancy is one of the primary population health indicators and in turn increases in life expectancy indicate improvements in population health and human welfare. Therefore, one of the ultimate goals of the countries is to increase the life expectancy. This article studies the effect of education and income inequalities, ICT indicators, CO2 emissions, and real GDP per capita on life expectancy in the new EU members for the period of 2010-2022 by employing fixed effects regression. The coefficients of panel regression uncover that education and income inequalities and CO2 emissions negatively impact life expectancy, but ICT indicators of internet usage and mobile cellular subscriptions and real GDP per capita positively affects the life expectancy. The findings of the panel regression analysis indicate that public policies to decrease the inequalities in education and income will make a contribution to life expectancy.

The effect of forest composition on outdoor recreation.

Climate change will shift the composition of northern Minnesota forests from boreal to temperate by the end of the century. This shift in forest composition will likely affect outdoor recreation, a valuable ecosystem service and a key economic driver for the region. In this context, the objective of our paper is to empirically examine the relationship between forest composition and recreation. We analyze the effect of changes in forest composition for seven forest types on seven types of recreation using a lognormal pooled panel regression model for Minnesota's Laurentian Mixed Forest Province. Earlier research showed forest composition affected recreation at the level of broad groups of broadleaved or coniferous species. We find a statistically significant empirical association between forest composition and recreation at the forest type level (forest types within those broad groups). This relationship varies across forest types and recreation categories. For example, big game hunting is positively related to elm-ash-cottonwood and white-red-jack pine and negatively associated with aspen-birch. We find individual forest types within broad groups of broadleaved or coniferous forests, have different relationships with recreation, so that these broad groups are not sufficient in capturing the effect of forest composition on recreation. Our results are of interest in the context of current shifts in forest composition caused by climate change, which could also affect recreation. Our findings suggest adding a forest composition lens to existing policies could facilitate strategies for more effective recreation management and climate change adaptation.

The influence mechanism of resource sharing on tourism industry innovation.

The development of the concept of "sharing" in the era of the digital economy has promoted significant changes in the innovation mechanism of the tourism industry. This article takes 31 provinces (municipalities and districts) in China as the research object, and collect four types of tourism statistics. Inspired by resource-based theory and dynamic capability theory, the article builds a research model of resource sharing for tourism industry innovation, proposes relevant hypotheses, discusses the influence mechanism of tourism industry innovation, and uses a fixed effects model and intermediary mechanism model for empirical test. The results show that the constructed model has good explanatory power and adaptability. Resource sharing can significantly drive tourism industry innovation, and the conclusion is robust. Tourism industry marketization, innovation ability, and competitiveness play an intermediary role in resource sharing to promote tourism industry innovation. The influence of resource sharing on tourism industry innovation has regional heterogeneity. The article aims to reveal the influence mechanism of resource sharing on tourism industry innovation under the digital economy, and the research conclusions can provide references for various provinces (municipalities and districts) to improve regional industrial innovation ability.

Determination of the best geographic weighted function and estimation of spatio temporal model - Geographically weighted panel regression using weighted least square.

This study proposes the development of a spatio-temporal model with geographic weights containing elements of location, time and the correlation between the two. The spatio-temporal model is a spatial regression model that combines geographic information and time series simultaneously. The model can overcome the problem of spatial heterogeneity and spatial effects. The spatial temporal model used is the Geographically Weighted Panel Regression (GWPR) model with a within estimator. Therefore, it is necessary to determine the best geographic weighting with the optimal bandwidth value and the lowest Cross Validation (CV). The geographic weights used were the Gaussian kernel function, the Bisquare kernel function and the exponential kernel function. Estimation of spatio-temporal model parameters using Weighted Least Square (WLS). The GWPR model was applied to food security index data in 34 Indonesian provinces. The problem of food security is an important problem to be solved in Indonesia, one way is to find the factors that influence the food security index through spatio-temporal modeling. This study consists of data exploration, descriptive statistics, spatial mapping distribution, selection of geographic weights and GWPR modeling. The results showed that the spatio temporal statistical model of GWPR was more accurate with a good model of 92.78 % and a Root mean Square Error value of 3.41. Some highlights of the proposed approach are.

Dynamic evolution and spatial difference of public health service supply in economically developed provinces of China: typical evidence from Guangdong Province.

OBJECTIVE: The outbreak of the COVID-19 pandemic has drawn attention from all sectors of society to the level of public health services. This study aims to investigate the level of public health service supply in the four major regions of Guangdong Province, providing a basis for optimizing health resource allocation. METHODS: This article uses the entropy method and panel data of 21 prefecture-level cities in Guangdong Province from 2005 to 2021 to construct the evaluation index system of public health service supply and calculate its supply index. On this basis, the standard deviation ellipse method, kernel density estimation, and Markov chain are used to analyze the spatiotemporal evolution trend of the public health service supply level in Guangdong Province. The Dagum Gini coefficient and panel regression model are further used to analyze the relative differences and the key influencing factors of difference formation. Finally, the threshold effect model is used to explore the action mechanism of the key factors. RESULTS: Overall, the level of public health service supply in Guangdong Province is on an upward trend. Among them, polarization and gradient effects are observed in the Pearl River Delta and Eastern Guangdong regions; the balance of public health service supply in Western Guangdong and Northern Mountainous areas has improved. During the observation period, the level of public health services in Guangdong Province shifted towards a higher level with a smaller probability of leapfrogging transition, and regions with a high level of supply demonstrated a positive spillover effect. The overall difference, intra-regional difference and inter-regional difference in the level of public health service supply in Guangdong Province during the observation period showed different evolutionary trends, and spatial differences still exist. These differences are more significantly positively affected by factors such as the level of regional economic development, the degree of fiscal decentralization, and the urbanization rate. Under different economic development threshold values, the degree of fiscal decentralization and urbanization rate both have a double threshold effect on the role of public health service supply level. CONCLUSION: The overall level of public health service supply in Guangdong Province has improved, but spatial differences still exist. Key factors influencing these differences include the level of regional economic development, the degree of fiscal decentralization, and the urbanization rate, all of which exhibit threshold effects. It is suggested that, in view of the actual situation of each region, efforts should be made to build and maintain their own advantages, enhance the spatial linkage of public health service supply, and consider the threshold effects of key factors in order to optimize the allocation of health resources.

The Subglottic Stenosis 6 Questionnaire: A Novel Quality-of-Life Survey Validated to Predict Need for Intervention in Patients With Idiopathic Subglottic Stenosis.

BACKGROUND: Idiopathic subglottic stenosis (iSGS) is a recurrent, chronic disease defined by fibroinflammatory narrowing of the subglottic airway. A key challenge in treatment is monitoring disease progression, which may be debilitating and unpredictable in its timing. RESEARCH QUESTION: Can the Subglottic Stenosis 6 (SGS-6) questionnaire be validated as a novel quality-of-life (QOL) instrument to monitor breathing, disease progression, and disease severity proactively in patients with iSGS? STUDY DESIGN AND METHODS: Panel data from 51 patients with iSGS were collected from January 2012 through June 2022, representing 1,684 patient encounters including routine office visits and treatment encounters. Subjective QOL scores (including the novel SGS-6 and established RAND-36 and EuroQol Five Dimensions [EQ-5D] Visual Analog Scale) and objective pulmonary function test (PFT) results were collected at each visit. Subjective SGS-6 QOL scores were repeated within 1 week of initial reporting. Panel regression analyses were performed to assess the relationship between SGS-6 scores, PFT results, and a patient's need for intervention. Minimal clinically important differences (MCIDs) for SGS-6 and peak expiratory flow percentage (PEF%) were assessed using receiver operating characteristic (ROC) curve analysis and a patient's need for intervention as the external anchor. RESULTS: Each one-point increase in SGS-6 score (of a maximum of 27) was associated with a 3.26% decrease in PEF%, a 1.93-point decrease in RAND-36 Physical Health composite score, a 1.27-point decrease in RAND-36 Mental Health composite score, and a 0.88-point decrease in EQ-5D Visual Analog Scale score. The intracorrelation coefficient for the SGS-6 composite score is 0.838 (95% CI, 0.770-0.888). Compared with patient baselines, SGS-6 scores were 4.66 points greater at the time of intervention with an MCID of 2.25 from a patient's baseline. The area under the ROC curve for SGS-6 and a patient's intervention point was 0.81. INTERPRETATION: iSGS disease severity can be modeled using the SGS-6 questionnaire, offering physicians and patients a potentially new method of tracking disease progression and need for intervention remotely.

The relationship between PM10 and SO2 exposure and Covid-19 infection rates in Turkey using nomenclature of territorial units for statistics level 1 regions.

The Covid-19 pandemic, which has been affecting the world since December 2019, has become one of the biggest problems of the 21st century. There are studies stating that the contagiousness of the Covid-19 pandemic, which is transmitted from person to person, increases more with environmental factors such as air pollution, and accordingly, there is an increase in the number of cases. In this study, a panel regression model to investigate the effect of air pollution concentrations such as PM10 and SO2 as environmental factors and population density on the monthly mean number of Covid-19 cases for 12 regions at the nomenclature of territorial units for statistics (NUTS) level 1 in Turkey between June 2020 and November 2020, and a linear regression model to investigate the effect at the regional level. we used. Based on the model results, we concluded that a small increase in air pollution indicators led to an increase in the number of Covid-19 cases in Turkey and its regions. It is very important to identify preventable environmental factors in order to prevent and minimize the effects of respiratory tract diseases and rapidly spreading pandemic diseases such as Covid-19. Accordingly, we can conclude that countries should take some measures, especially on air pollution, in order to develop public health and pandemic/disease management strategies and to reduce the risk of respiratory diseases.

Digital inclusive finance, government intervention, and urban green technology innovation.

Digital inclusive finance eases credit constraints on innovative small and medium-sized enterprises which contributes to urban green technology innovation in China. Government intervention plays an essential role in the development of digital inclusive finance. Based on the panel data of 243 cities in China from 2011 to 2019, this paper empirically examines the relationship between digital inclusive finance and urban green technology innovation as well as the intrinsic mechanism of government intervention. The findings show that, even after a series of robustness tests, digital inclusive finance can promote the quantity and quality of green technology innovation. In terms of its mechanism, digital inclusive finance can empower green technology innovation by complementing traditional financial development and stimulating green consumption, both in terms of supply and demand. At the same time, in the dynamic process of digital financial inclusion from low to high, the development of traditional finance and green consumption level on the marginal promotion of green technology innovation continues to strengthen. The nonlinear relationship test reveals that there is a significant double threshold effect on the positive impact of digital inclusive finance on urban green technology innovation with the evolution of government intervention. The innovation incentive effect of digital financial inclusion will be marginal decreasing with the increase of government intervention. Further considering the heterogeneity of urban geographic location and environmental regulation, it is found that digital financial inclusion promotes green technology innovation more in eastern and high-environmental regulation cities. Based on the above research conclusions, this paper argues that while developing digital inclusion finance, government support policies should be adjusted promptly to constantly stimulate the "Metcalfe's law" effect of digital inclusive finance enabling green technology innovation.

Spatiotemporal evolution and influencing factors of the allocation of social care resources for the older adults in China.

BACKGROUND: The reasonable allocation of social care resources for the older adults is a key measure to actively respond to population aging. This study aims to evaluate the evolutionary trend, spatial differences and influencing factors of the social elderly care resources (SECR) allocation in China. METHODS: This study constructed a comprehensive index system consisting of three dimensions: material resources, human resources and financial resources, to measure the level of SECR in mainland China. The Kernel density estimation was used to reveal the dynamic evolution trend, and Dagum Gini Coefficient and its decomposition method were used to investigate the equity of SECR allocation. Spatial panel regression models were used to analyze the influencing factors of the allocation of SECR. RESULTS: The level of SECR is rising from 0.197 in 2013 to 0.208 in 2019. The middle-high- and high-level areas of SECR were mainly distributed in the eastern and western China. The Gini coefficient of SECR decreased from 0.262 in 2013 to 0.249 in 2019. Per capita GDP, the proportion of social welfare expenditure in GDP and the proportion of the tertiary industry in GDP have significant positive effects on the allocation of SECR. Population aging and the development of service industry exhibit significant negative spatial spillover effects on the allocation of SECR. CONCLUSIONS: The fairness of the allocation of SECR in China has been improved, while the spatial distribution is imbalanced. Economic development, fiscal input and the development of service industry have significant positive effects while population aging has significant negative effects on the SECR allocation.

Driving impact and spatial effect of the digital economy development on carbon emissions in typical cities: a case study of Zhejiang, China.

The digital economy (DE) not only drives economic innovation and development but also has significant environmental effects by promoting lower carbon emissions. To investigate the spatial effects of DE on urban carbon emissions, this study comprehensively measures the level of DE development based on the panel data from 11 typical cities in Zhejiang Province from 2011 to 2020, by comparing analysis using different regression models. The following conclusions are obtained: (1) The total carbon emissions (TC) of Zhejiang cities in general show a fluctuating change trend of first increasing and then slowly decreasing, while carbon emission intensity and carbon emission per capita in general show a fluctuating change trend of decreasing. Cities with high TC are primarily concentrated in the Hangzhou Bay city cluster, accounted for 62 ~ 65% of the province's carbon emissions. The development of the DE in Zhejiang cities shows steady growth, but there are large differences among cities, with Hangzhou and Ningbo standing out as particularly prominent. (2) There is a significant inverted U-shaped relationship between the DE and the level of carbon emissions in Zhejiang Province. The influence coefficient of the DE on the primary term of TC is 0.613, and the influence coefficient of the quadratic term of TC is - 1.008. (3) In terms of the spatial spillover effect of the DE on carbon emissions, the study finds that compared to the direct effect, the spatial spillover effect is not significant. However, the allocation of transport resources shows a positive spatial spillover effect (increasing carbon emissions, coefficient value is 0.138), while technological progress shows a somewhat negative spatial spillover effect (decreasing carbon emissions, coefficient value is - 0.035). (4) The study also finds that the smart city pilot policy significantly reduces urban carbon emissions. Moreover, the effect of the DE on carbon emissions is confirmed through the significance test of the quadratic term when replacing the geographical and economic distance weight matrices. This indicates that the empirical findings are robust to these tests. Finally, several countermeasures to reduce carbon emissions are proposed from the perspective of DE development.

Maternal health, well-being, and employment transitions: A longitudinal comparison of partnered and single mothers in Germany.

Balancing parenthood and employment can be challenging and distressing, particularly for single mothers. At the same time, transitioning to employment can improve the financial situations of single mothers and provide them with access to social networks, which can have beneficial effects on their health and well-being. Currently, however, it is not well understood whether the overall impact of employment on single mothers is positive or negative, and to what extent it differs from the impact of employment on partnered mothers. Building on the literature on work-family conflict, we investigate the differential effects of employment transitions on the health and well-being of single mothers and partnered mothers. Using longitudinal data from the German Socio-Economic Panel (1992-2016), we apply panel regression techniques that address the potential endogeneity of maternal employment, as well as the dynamic nature of the relationship between employment transitions and maternal health and well-being. We find that employment has a positive impact on single mothers, and that single mothers benefit from employment significantly more than partnered mothers. Surprisingly, income does not appear to be an important driver of these results. Overall, our findings suggest that employment plays a key role in the well-being of single mothers.

Sub-regional variation in atmospheric and land variables regulates tea yield in the Dooars region of West Bengal, India.

Climatic variables can have localized variations within a region and these localized climate patterns can have significant effect on production of climate-sensitive crops such as tea. Even though tea cultivation and industries significantly contribute to employment generation and foreign earnings of several South Asian nations including India, sub-regional differences in the effects of climatic and soil variables on tea yield have remained unexplored since past studies focused on a tea-producing region as a whole and did not account for local agro-climatic conditions. Here, using a garden-level panel dataset based on tea gardens of Dooars region, a prominent tea-producing region in India, we explored how sub-regional variations in climatic and land variables might differently affect tea yield within a tea-producing region. Our analysis showed that the Dooars region harboured significant spatial variability for different climatic (temperature, precipitation, surface solar radiation) and soil temperature variables. Using graph-based Louvain clustering of tea gardens, we identified four spatial sub-regions which varied in terms of topography, annual and seasonal distribution of climatic and land variables and tea yield. Our sub-region-specific panel regression analyses revealed differential effects of climatic and land variables on tea yield of different sub-regions. Finally, for different emission scenario, we also projected future (2025-2100) tea yield in each sub-region based on predictions of climatic variables from three GCMs (MIROC5, CCSM4 and CESM1(CAM5)). A large variation in future seasonal production changes was projected across sub-regions (-23.4-35.7% changes in premonsoon, -4.2-3.1% changes in monsoon and -10.9-10.7% changes in postmonsoon tea production, respectively).

A quantile panel examination of the moderation effects of guardianship on residential burglary.

Do informal and formal mechanisms of guardianship work together to reduce residential burglary? In this article we argue that informal guardianship moderates the relationship between formal mechanisms of guardianship and residential burglary. Formal guardianship requires some level of social cohesion and trust to be effective against residential burglary. We test this argument with the use of robust panel quantile methods controlling for time effects, spatial effects, and alternative explanations. Using Mexico City neighborhood crime and census data, we show evidence of a moderating weakening effect of informal guardianship on the previous relationship, particularly in deprived neighborhoods and only in the upper quantiles of the residential burglary distribution. In addition, the moderation effects seem to have weakened over time. In sum, the combination of guardianship mechanisms seems to have been more effective in high burglary risk deprived neighborhoods, although their combination seems to have become less relevant.

Differential Fiscal Performances of Plausible Disaster Events: A Storyline Approach for the Caribbean and Central American Governments under CCRIF.

Fiscal resilience against disasters is vital for the recovery in the aftermath of climate hazards. Without swift access to available funds for disaster relief, damages to human and the economy would be further exacerbated. How insurance may influence fiscal performance over time and can increase fiscal resilience for today and under a future climate has not been looked at yet in detail. Focusing on the Caribbean region and on the fiscal performance of governments after disaster events, we empirically analyze the effectiveness of the Caribbean Catastrophe Risk Insurance Facility (CCRIF) regarding the reduction of short-term fiscal effects. We embed this analysis within a novel climate impact storyline approach where we produce past plausible events and investigate the usefulness of insurance under such events. The storylines were modified according to global and climate change related boundary conditions to address the issue whether the CCRIF is fit for purpose or will need to be adapted in the future. We found that both hurricane strikes and the CCRIF affect fiscal outcomes of Caribbean countries. Furthermore, there are indications that CCRIF can counteract the negative fiscal consequences over the short term period induced by the disaster. Our analysis should shed some light on the current discussions on how development related assistance can be structured to enhance climate resilience in highly exposed countries for both direct and fiscal impacts of disasters. Supplementary Information: The online version contains supplementary material available at 10.1007/s41885-023-00126-0.

Spatial differentiation and driving factors of the high-quality development of undertakings for the aged of China.

BACKGROUND: Promoting the high-quality development (HQD) of undertakings for the aged is an important action to proactively respond to the rapid population aging in China. This study analyzes spatial differentiation and driving factors of the HQD of China's undertakings for the aged. METHODS: Based on a quantitative indicator system consisting of old-age social security, elder care services, health care service and older adults' social participation, the HQD levels of 31 Chinese provincial administrative regions during 2013-2019 are measured by using the entropy weight method. Spatial panel regression models are used to analyze the impact of population aging, economic development and digital technology on the HQD of undertakings for the aged. RESULTS: The comprehensive level of the HQD slightly increased from 0.212 to 2013 to 0.220 in 2019, and the overall level was low. The HQD of the eastern region was the highest (0.292), followed by the western region (0.215), and the central region was the lowest (0.151). The high-high cluster type was mainly distributed in the eastern region; the low-low cluster type was mainly distributed in the western and central regions. Economic development and digital technology have significant positive effects while population aging has significant negative effects on the HQD of undertakings for the aged. CONCLUSION: There is a significantly spatial differentiation in the HQD of China's undertakings for the aged. In order to promote the HQD of undertakings for the aged, it is necessary to identify development gaps through making HQD evaluation and to focus on the indicators that are critical in maintaining sustainable economic development and to develop digital technology in order to get rid of those gaps.

Seeing the forest and the trees: Holistic view of social distancing on the spread of COVID-19 in China.

The human social and behavioral activities play significant roles in the spread of COVID-19. Social-distancing centered non-pharmaceutical interventions (NPIs) are the best strategies to curb the spread of COVID-19 prior to an effective pharmaceutical or vaccine solution. This study investigates various social-distancing measures' impact on the spread of COVID-19 using advanced global and novel local geospatial techniques. Social distancing measures are acquired through website analysis, document text analysis, and other big data extraction strategies. A spatial panel regression model and a newly proposed geographically weighted panel regression model are applied to investigate the global and local relationships between the spread of COVID-19 and the various social distancing measures. Results from the combined global and local analyses confirm the effectiveness of NPI strategies to curb the spread of COVID-19. While global level strategies allow a nation to implement social distancing measures immediately at the beginning to minimize the impact of the disease, local level strategies fine tune such measures based on different times and places to provide targeted implementation to balance conflicting demands during the pandemic. The local level analysis further suggests that implementing different NPI strategies in different locations might allow us to battle unknown global pandemic more efficiently.

Quantifying the impact of COVID-19 on travel behavior in different socio-economic segments.

The COVID-19 pandemic has resulted in substantial negative impacts on social equity. To investigate transport inequities in communities with varying medical resources and COVID controlling measures during the COVID pandemic and to develop transport-related policies for the post-COVID-19 world, it is necessary to evaluate how the pandemic has affected travel behavior patterns in different socio-economic segments (SES). We first analyze the travel behavior change percentage due to COVID, e.g., increased working from home (WFH), decreased in-person shopping trips, decreased public transit trips, and canceled overnight trips of individuals with varying age, gender, education levels, and household income, based on the most recent US Household Pulse Survey census data during Aug 2020 âˆ¼ Dec 2021. We then quantify the impact of COVID-19 on travel behavior of different socio-economic segments, using integrated mobile device location data in the USA over the period 1 Jan 2020-20 Apr 2021. Fixed-effect panel regression models are proposed to statistically estimate the impact of COVID monitoring measures and medical resources on travel behavior such as nonwork/work trips, travel miles, out-of-state trips, and the incidence of WFH for low SES and high SES. We find that as exposure to COVID increases, the number of trips, traveling miles, and overnight trips started to bounce back to pre-COVID levels, while the incidence of WFH remained relatively stable and did not tend to return to pre-COVID level. We find that the increase in new COVID cases has a significant impact on the number of work trips in the low SES but has little impact on the number of work trips in the high SES. We find that the fewer medical resources there are, the fewer mobility behavior changes that individuals in the low SES will undertake. The findings have implications for understanding the heterogeneous mobility response of individuals in different SES to various COVID waves and thus provide insights into the equitable transport governance and resiliency of the transport system in the "post-COVID" era.

Impact of anti-dumping on global embodied air emissions: a complex network perspective.

After the rise of trade protectionism, anti-dumping has become a common means of political and trade games between countries. Global supply chains move production emissions between countries or regions through trade. In the context of carbon neutrality, anti-dumping measures representing the right to trade may become a tool for the game of emission rights between countries. Therefore, it is very important to study the environmental effects of anti-dumping to cope with global climate change and promote national development. Taking a sample of 189 countries and regions from the EORA input-output table with a study period of 2000-2016, we use the complex network, multi-regional input-output and panel regression models to verify the impact of anti-dumping on air emission transfer by constructing an anti-dumping network and an embodied air emission network. The results show that the initiator of anti-dumping can use anti-dumping to realize the cross-border transfer of ecological costs, reduce the burden of emission reduction and save more on emission quota. Developing countries lacking the right to speak in trade will increase the export volume of commodities after being subjected to a large number of anti-dumping sanctions, thus paying higher ecological costs and consuming more emission quotas. From a global perspective, additional emissions from product production can further contribute to global climate change.