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The construction industry plays an important role in energy saving and carbon emissions mitigation of China. Promoting carbon emission efficiency is seen as an efficient way to abate carbon emissions. Using 2005-2016 data, the carbon emission efficiency of the construction sector in 30 provinces is estimated, and the spatial distribution characteristics of the carbon emission efficiency of the construction industry is explored. The spatial Markov transition probability matrix is employed to investigate the influence of the spatial spillover effect on the regional distribution pattern of carbon emission efficiency. The results demonstrate that the carbon emission efficiency of the construction industry exhibits an unbalanced regional distribution, which is high in the east and low in the west. The spatial autocorrelation indicates that the carbon emission efficiency has a spatial dependence and is characterized by spatial agglomeration. Markov Chain results show a significant spatial spillover effect in carbon emission efficiency. The provinces with higher carbon emission efficiency have a positive effect on their neighbors, while the provinces with lower efficiency have a negative effect on neighbors. The findings are of great importance to understand the differences in and interactions of carbon emission efficiency between regions.

In this study, we adopt kernel density estimation, spatial autocorrelation, spatial Markov chain, and panel quantile regression methods to analyze spatial spillover effects and driving factors of carbon emission intensity in 283 Chinese cities from 1992 to 2013. The following results were obtained. (1) Nuclear density estimation shows that the overall average carbon intensity of cities in China has decreased, with differences gradually narrowing. (2) The spatial autocorrelation Moran’s I index indicates significant spatial agglomeration of carbon emission intensity is gradually increasing; however, differences between regions have remained stable. (3) Spatial Markov chain analysis shows a Matthew effect in China’s urban carbon emission intensity. In addition, low-intensity and high-intensity cities characteristically maintain their initial state during the transition period. Furthermore, there is a clear “Spatial Spillover” effect in urban carbon emission intensity and there is heterogeneity in the spillover effect in different regional contexts; that is, if a city is near a city with low carbon emission intensity, the carbon emission intensity of the first city has a higher probability of upward transfer, and vice versa. (4) Panel quantile results indicate that in cities with low carbon emission intensity, economic growth, technological progress, and appropriate population density play an important role in reducing emissions. In addition, foreign investment intensity and traffic emissions are the main factors that increase carbon emission intensity. In cities with high carbon intensity, population density is an important emission reduction factor, and technological progress has no significant effect. In contrast, industrial emissions, extensive capital investment, and urban land expansion are the main factors driving the increase in carbon intensity.

This paper focuses on the spatiotemporal evolution characteristics, as well as the driving factors, of carbon emissions in the prefecture-level cities in the Yellow River Basin (YB). The paper’s findings will aid in promoting ecological conservation and high-quality development in the region. The initiatives undertaken in the YB are a significant national strategy towards achieving carbon peaking and carbon neutrality. To fully investigate the spatiotemporal evolution process, as well as the typical characteristics of their carbon emissions, conventional, and spatial Markov transition probability matrices were developed utilizing YB’s panel data for 55 prefecture-level cities from 2003 to 2019. The generalized Divisia index decomposition method (GDIM) cleverly uses this data to conduct a complete analysis of the dynamics and driving processes influencing the change in carbon emissions in these cities. However, the evolution of carbon emissions in prefecture-level cities has reached a point of stability that maintains the original state, making it challenging to make meaningful short-term progress. The data indicates that prefecture-level cities in the YB are emitting more carbon dioxide on average. Neighborhood types in these cities significantly influence the transformation of carbon emissions. Low-emission areas can encourage a reduction in carbon emissions, whereas high-emission areas can encourage an increase. The spatial organisation of carbon emissions exhibits a “high-high convergence, low-low convergence, high-pulling low, low-inhibiting high” club convergence phenomenon. Carbon emissions rise with per capita carbon emissions, energy consumed, technology, and output scale, whereas it falls with carbon technology intensity and output carbon intensity. Hence, instead of enhancing the role of increase-oriented variables, prefecture-level cities in the YB should actively engage these reduction-oriented forces. The YB’s key pathways for lowering carbon emissions include boosting research and development, promoting and applying carbon emission reduction technologies, lowering output carbon intensity and energy intensity, and improving energy use effectiveness.

Green innovation in the tourism industry is a sustainable development concept for resource conservation and environmental optimization. The effective measurement of green innovation efficiency in the tourism industry and an accurate understanding of its spatial relationship was significantly important for promoting its sustainable development. Using the SBM-undesirable model, kernel density estimation, and a spatial Markov chain, we explored the spatio-temporal evolution characteristics and influencing mechanisms of urban tourism green innovation efficiency (TGIE) in China between 2000 and 2020. We found that (1) the temporal and spatial changes of TGIE were generally at a lower than medium level and fluctuated throughout country, with a transition in the east, collapse in the middle, and stagnation in the northeast. (2) The dynamic evolution of TGIE always exhibited polarization, but regional coordination was gradually enhanced with strong stability, although it was difficult to achieve leap-forward development. The cities with spatial upward transfer were concentrated mainly in the central and western region and while there were few cities with a downward adjustment, there were obvious asymmetrical spatial spillover effects. (3) The driving factors of TGIE were the overall economic level, industrial structure, government regulation, and education level. These factors had a significant positive relationship with TGIE, while the degree of opening up to the outside world has no significant effect, but the degree of influence, mechanism, and conditions of each factor were strongly regional.

The digital economy is critical to national economic growth and high-quality economic development. It is theoretically and practically significant to measure the development level and spatial differences in the digital economy to promote the construction of a digital China. This study constructed a digital economy evaluation index and analyzed the dynamic evolution, spatial differences, and driving factors of China’s provincial digital economy from 2011 to 2020 using a spatial Markov chain, the Dagum Gini coefficient, and geographical detector methods. The results demonstrated that China’s provincial digital economy grew from 2011 to 2020. The spatial distribution of the digital economy was high in eastern provinces and municipalities such as Beijing, Shanghai, Guangdong, Jiangsu, and Zhejiang, and low in central and western provinces and autonomous regions. The probability of upward transfer in developing China’s provincial digital economy was greater than that of preserving the original state, and China’s provincial digital economy has great potential for development. A region with a medium-high level in the digital economy is more likely to achieve high-level development when neighboring regions are characterized by a medium-high or high level of digital economy development, as the spillover effects from the neighbors may be strongly favorable and the region takes advantage of its developed surroundings. There were significant spatial differences in the development of China’s provincial digital economy, caused primarily by inter-regional differences. The spatial differentiation of China’s provincial digital economy was caused by the interaction of multiple factors, led by economic conditions and R&D expenditure.

Food security is the cornerstone of national security. It is urgent to scientifically and reasonably construct a food security assessment system and objectively measure the actual development level of China’s food security. This is an important issue to enhance the modernization level of China’s food security governance system and governance capacity. Constructing evaluation indicators for food security from a theoretical perspective, analyzing the spatiotemporal changes in China’s food security, and expanding the theoretical perspective of food security research. At the same time, it provides ideas for the construction and improvement of China’s long-term food security guarantee mechanism, and also provides scientific reference for world food security. The food security level of 30 provinces and cities in China from 2011–2021 was measured by using the Entropy weight TOPSIS model. The spatial characteristics and regional differences of China’s food security were analyzed using the Standard Deviation Ellipse technology, Moran’s index and Dagum Gini coefficient. The dynamic evolution process of food security level development was based on Markov Chain model. The food security scores of all regions in China showed three stages of fluctuation, balance and growth from 2011 to 2021. The spatial distribution of China’s food security level was developing from northeast to southwest, and shows spatial autocorrelation. Regional differences were the main source of regional disparities in China’s food security level. The evolution of China’s food security level had shown significant spatial self strengthening characteristics and spatial spillover effects. The spatial pattern of China’s food security presented a significant expansion feature along the ‘Northeast Southwest’ axis. The spatial differentiation of food security was showing a strengthening trend, and the regional coordination mechanism urgently needs to be improved. The root cause of regional differences lied in the imbalance between main production and main sales areas, with a dual lack of policy compensation and market mechanisms.

Accelerating the coordinated development of the high-quality development and carbon emission (HQD-CE) system in China’s construction industry is of great significance in achieving carbon peak and carbon neutrality. The coupling coordination degree model (CCDM) was constructed, and spatial and temporal distribution characteristics and dynamic evolution laws of the coupled and coordinated development of HQD-CE of the construction industry in 30 provinces in China from 2012 to 2021 were explored by using spatial autocorrelation and spatial Markov chain. Results show the following: (1) The CCD showed an increasing trend, and the spatial pattern was higher in the southeast and lower in the northwest. (2) The spatial autocorrelation of CCD was significant, and the club effect was obvious, which made it difficult to realize the hierarchical leap in a short period. (3) The spatial spillover effect of CCD was significant, provinces with basic coupling dissonance faced the risk of horizontal solidification, and there were too few provinces with high-quality coupling coordination to drive the others, which could result in provinces with basic coupling coordination being influenced by provinces with basic coupling dissonance and falling back in development. The conclusions of this study can provide a reference basis for the policy formulation of low-carbon development in the construction industry.

An environmental problem which is of concern across the globe nowadays is air pollution. The extent of air pollution is often studied based on data on the observed level of air pollution. Although the analysis of air pollution data that is available in the literature is numerous, studies on the dynamics of air pollution with the allowance for spatial interaction effects through the use of the Markov chain model are very limited. Accordingly, this study aims to explore the potential impact of spatial dependence over time and space on the distribution of air pollution based on the spatial Markov chain (SMC) model using the longitudinal air pollution index (API) data. This SMC model is pertinent to be applied since the daily data of API from 2012 to 2014 that have been gathered from 37 different air quality stations in Peninsular Malaysia is found to exhibit the property of spatial autocorrelation. Based on the spatial transition probability matrices found from the SMC model, specific characteristics of air pollution are studied in the regional context. These characteristics are the long-run proportion and the mean first passage time for each state of air pollution. It is found that the probability for a particular station’s state to remain good is 0.814 if its neighbors are in a good state of air pollution and 0.7082 if its neighbors are in a moderate state. For a particular station having neighbors in a good state of air pollution, the proportion of time for it to continue being in a good state is 0.6. This proportion reduces to 0.4, 0.01, and 0 for the cell of moderate, unhealthy, and very unhealthy states, respectively. In addition, there exists a significant spatial dependence of API, indicating that air pollution for a particular station is dependent on the states of the neighboring stations.

There are important ways to solve the ecological risk problems of regional water resources and soil resources, and to promote the benign development of soil and water resources, involving scientific evaluation of the ecological risk of soil and water resources in Hefei metropolitan area, clarifying the intrinsic evolution law of ecological risk and identifying the characteristics of spatial and temporal variations. Based on the conceptual model of “ST-QS-RR”, the evaluation indicator system is constructed, the CRITIC method is used to assign weights, and the TOPSIS method, kernel density method, markov chain and resistance model are used to measure and analyse the spatial and temporal characteristics of ecological risk of soil and water resources, and to explore the main factors that cause ecological risk of soil and water resources. The results of the study show that: (1) Hefei metropolitan area and its cities show a steady decline and the characteristics of “high in the north and low in the south, high in the west and low in the east”. (2) Most of the subsystems in the Hefei metropolitan area and the cities show a decreasing trend, with its resistance factors mainly concentrated in the QS system. (3) There is club convergence in Hefei metropolitan area. When the type of adjacent domain is higher, the change of risk type is more sensitive.

Agricultural ecological efficiency is not only the key link between green development and high-quality development of agriculture, but also an important regulatory indicator for China’s rural revitalization. Based on provincial panel data of China from 2000 to 2019, using land, mechanical, labor, fertilizer, pesticide, and agricultural film as input variables and economic output and agricultural carbon emissions as output variables, the inter-provincial agricultural ecological efficiency is calculated by a super-efficient SBM model, and the traditional spatial Markov probability transfer matrices are constructed based on time series and spatial correlation analyses. By exploring the spatial and temporal dynamic evolution characteristics of agricultural ecological efficiency, it is found that the agricultural ecological efficiency of China increased steadily with fluctuations. In addition, the provincial gap has been narrowing, but the overall level is still at a low level; thus, there is still a large space for improvement in agricultural ecological efficiency. The overall trend of agricultural ecological efficiency shifting to a high level in China is significant, but its evolution has the stability to maintain the original state, and achieving leapfrog transfer is relatively hard. The geospatial pattern plays an important role in the spatial-temporal evolution of agricultural ecological efficiency, with significant spatial agglomeration characteristics. Provinces with high agricultural ecological efficiency enjoy positive spillover effects, while provinces with low agricultural ecological efficiency have negative spillover effects; thus, gradually forming a “club convergence” phenomenon of “high agglomeration, low agglomeration, high radiation, and low suppression” in the spatial pattern. In addition, support for the improvement of agricultural ecological efficiency will be provided in this study.