Spatial Markov Chain Research Articles

Analysis of spatial and temporal characteristics and evolution of green total factor productivity in agriculture in the lower Yellow River basin

The construction of ecological barriers in the Yellow River Basin represents a significant step toward reducing agricultural carbon emissions, achieving carbon neutrality, and reaching carbon peaking in China. The diverse agrarian development objectives of various regions within the basin have resulted in a heterogeneous approach to greening agriculture. Therefore, this paper will evaluate the development of carbon sink agriculture across 34 cities and municipalities in the lower Yellow River basin from 2008 to 2021 based on the EBM-GML model, and analyze the spatial-temporal evolution of agricultural green total factor productivity (AGTFP) in each region through the application of the Moran index, kernel density estimation, and spatial Markov chain analysis. The results demonstrate that agricultural carbon emissions in the Lower Yellow River Basin gradually decreased throughout the study period. Furthermore, overall carbon emission efficiency improved, indicating significant potential for further emission reduction. In addition, Agricultural Green Technology Progress (AGTC) has become a primary driver of AGTFP growth, while Agricultural Green Technology Efficiency (AGEC) has demonstrated a gradual upward trend. Locally, most areas are weakly connected and display an isolated development trend. The results of the kernel density analysis demonstrate a notable degree of mobility in the distributional dynamics of AGTFP growth, characterized by a gradual narrowing of the gap between locations. The transfer of (AGTFP) types in the lower reaches of the Yellow River Basin is stable, with a noticeable “club convergence” phenomenon, while geographical conditions significantly influence the transfer of AGTFP types in this region. Based on long-term trend predictions, the future trajectory of AGTFP in the lower Yellow River Basin appears optimistic and is expected to improve progressively, with the overall distribution tending toward equilibrium.

Spatial-Temporal Evolution and Determinants of Green Economy Efficiency: An Integrated Analytical Approach

In response to global environmental challenges and the need for sustainable development, this study investigates the spatiotemporal evolution and determinants of green economy efficiency (GEE) across 30 Chinese provinces from 2005 to 2021. By employing a Slack-Based Measure (SBM) Data Envelopment Analysis (DEA) model, kernel density estimation, and spatial Markov chain analysis, this study provides a comprehensive assessment of GEE, incorporating both spatial and temporal dimensions. The analysis reveals a positive overall trend in GEE, with notable regional disparities, as eastern provinces exhibit significantly higher efficiency levels compared to central and western regions. Additionally, GEE improvements have been observed nationwide, although the central and western regions lag. Key factors influencing GEE include energy intensity, economic development, urbanization, industrialization, transport infrastructure, and government intervention, with distinct regional variations in their impact. The findings suggest that optimizing economic growth, reducing energy intensity, and fostering green urbanization and industrialization are essential strategies for enhancing GEE. This research contributes to the theoretical understanding of green economy efficiency and offers valuable policy recommendations for achieving balanced regional development and sustainable growth in China.

Spatio-temporal Situation, Regional Differences, and Dynamic Evolution of the Distribution of Ecological Compensation in the Yellow River Basin

In the context of regional coordinated development and modernization of ecological and environmental governance capacity, the spatial collaborative governance of transboundary river basins has received extensive attention, but the ecological compensation of river basins is faced with the intertwined relationship of rights and responsibilities, and the horizontal ecological compensation mechanism has not yet been perfected. Based on the emergy ecological footprint model, the ecological compensation amount of 90 cities in the Yellow River Basin in 2007-2021 was measured, and the spatiotemporal pattern, regional differences, and dynamic evolution characteristics of distribution were explored by combining exploratory spatial data analysis, Dagum Gini coefficient, kernel density estimation, and spatial Markov chain analysis. The results showed that： ① The spatiotemporal pattern of ecological compensation in the Yellow River Basin was different, the ecological compensation amount decreased slightly in the fluctuation and increased from the northwest to southeast gradient in general, and the high-value areas were concentrated in the areas with a superior ecological background or developed economy, and some upstream cities had given up many development opportunities to maintain the ecological security of the Yellow River Basin, however, failed to obtain reasonable ecological compensation. ② A significant positive spatial agglomeration phenomenon was observed in ecological compensation and the overall spatial distribution trend was "cold in the north and hot in the south." The number of low-payment areas and low-compensation areas gradually decreased, showing a gradually shrinking agglomeration layout from the periphery to the center, whereas the number of high-payment areas and high-compensation areas continued to increase and formed a spatial evolution characteristic of the coexistence of scattered distribution and group distribution. ③ The overall regional differences in ecological compensation expanded. The over-variation density was the main source of the overall differences, and the focus on alleviating the intra-regional and inter-regional differences was located in the downstream areas. ④ A slight multi-level differentiation phenomenon was present in ecological compensation. The influence of different neighbors on the horizontal transfer of ecological compensation was quite different. This spatial spillover effect easily formed a "space club convergence" phenomenon within a certain geographical spatial range, but with the expansion of time, the probability of maintaining the original level of ecological compensation in each city decreased, and mobility gradually increased. Therefore, increasing the investment in ecological compensation in different fields, strengthening the coordinated development between regions, and giving full play to the spatial spillover effect are important ways to solve the current spatial imbalance of ecological compensation in the Yellow River Basin.

Measurement of new qualitative productivity kinetic energy from the perspective of digital and green collaboration--comparative study based on European countries

At present, the world is witnessing the historic convergence of a new round of scientific and technological revolution and industrial change with the transformation of the development mode, and it is strategically important and far-reaching to rapidly enhance new qualitative productivity kinetic energy (NQPKE) on the basis of the lessons learning from the experience of the pioneering countries for all countries, so as to expand the room for manoeuvre of the economic development, obtain the initiative of international competition, and realise the benign interaction of high-quality development and high-level of security. On the basis of analysing the formation mechanism of NQPKE, we characterize NQPKE by the coupling and coordination degree of digital productivity and green productivity, we analyse NQPKE by using the entropy weight TOPSIS method and the coupling and synergistic model, and analyse the spatial-temporal characteristics, steady state development and constraints of NQPKE by using the method of kernel density estimation, the Markov chain, the spatial Markov chain, and the obstacle degree model based on data from 21 European countries from 2009 to 2022. The study finds that: (1) The NQPKE shows a wave-like development trend, and the polarization or multi-polarization state of the whole and the four major regions has been slowly weakened, with a certain degree of "club convergence" and "spatial spillover"; (2) The overall situation of barriers to NQPKE is relatively stable, the insufficient investment in scientific and technological development and weak technological innovation capacity are the most critical factors constraining the NQPKE in various countries. Based on the above conclusions, the article proposes to hold high the banner of peaceful development and win-win cooperation, give full play to the "club convergence" and "spatial spillover" effects of NQPKE, accelerate the development of NQPKE adapt to local conditions, and the "digital + green" double base will stimulate technological innovation, and the construction of a digital and green synergistic management system stimulates the potential of the new quality productivity of enterprises, ultimately unleash the surging kinetic energy of new quality productivity development.

Exploring spatiotemporal dynamics, seasonality, and time-of-day trends of PM2.5 pollution with a low-cost sensor network: Insights from classic and spatially explicit Markov chains

Fine particulate matter (PM2.5) is a major health and environmental concern, with significant spatiotemporal dynamics in urban areas. Low-cost air quality sensor (LCS) networks offer a paradigm-changing opportunity to acquire high spatiotemporal resolution data, revealing the urban pollution landscape with sufficient detail for effective policymaking and health assessment. This study advances geospatial air quality research by using classic and spatial Markov chains to analyze the seasonality and intra-daily variations of PM2.5 using LCS data. Results highlight distinctive PM2.5 seasonality, with the “Good” state predominating in summer and being least common in winter. Midday is the peak period for the “Good” state, while mornings and nights have poorer conditions, suggesting a need for stricter pollution control during evening traffic rush hours. Notably, the impact of temporal scale on spatial Markov analysis is substantial, showing a broader range of air pollution states, increased stability, and reduced variation between time intervals compared to daily assessments. Site-level analysis reveals that rural sites are more likely to maintain “Good” state and less likely to transition out of it. Overall, this study highlights the effectiveness of high spatiotemporal resolution data and demonstrates the capacity of Markov chains to reveal nuances in phenomena such as air pollution.

Urban low-carbon governance and ecological efficiency: new evidence from prefecture-level cities in China

IntroductionIn the search for sustainable development, urban eco-development is becoming a core agenda for all countries. China’s low-carbon city pilot (LCCP) policy is an important initiative to promote urban low-carbon governance. And exploring the direction of LCCP policy is an important step towards sustainable urban development.MethodsSuper-SBM is employed to calculate urban ecological efficiency using panel data from 254 prefecture-level cities between 2007 and 2020. The methods of kernel density estimation and spatial Markov chains are applied to the spatial analysis of urban ecological efficiency. The spatial analyses examine the spatiotemporal patterns and dynamic evolving trends of urban ecological efficiency. Additionally, the multiperiod difference-in-differences method is used to assess the impact of the LCCP policy on urban ecological efficiency. On this basis, we apply the method of mechanism analysis to discuss the influence mechanism.ResultsThe results indicate that urban ecological efficiency gradually forms a continuous spatial clustering pattern, although there is a widening tendency in the absolute differences. In the subsequent transfer process, the spatial factor plays a highly significant role. Benchmark regressions and auxiliary robustness tests demonstrate that the LCCP policy is effective in improving urban ecological efficiency. Further analysis shows that the effect of LCCP policies on ecological efficiency can be transmitted by promoting green technology innovation as well as facilitating a low-carbon transition in the energy mix. The above findings indicate the presence of the "Green Innovation Effect" and the "Decoupling Effect".DiscussionThese discoveries enhance the theoretical framework of urban ecology and offer valuable insights for other regions seeking to implement low-carbon urban governance.

Spatiotemporal evolution pattern and heterogeneity of resource-based city resilience in China

Resource-based cities (RBCs) encompass the east, middle, and west of China, covering an important economic hinterland. The improvement of RBC resilience can significantly impact the overall resilience of cities in China. This study analyzes the spatial characteristics and overall evolution trend of four types of RBCs based on regional differences and convergence theory. The results vivid showed sustainable development positively influenced growing-type RBCs also showed that the overall RBC resilience coupling coordination degree improved throughout the study period (2009–2019), and that the evolution of RBC resilience demonstrated good convergence characteristics. Furthermore, a distinct gap is evident between high resilience-level growing RBCs and relatively low-level regenerative RBCs. All RBCs will eventually converge to the third level with a higher level of coupling coordination. However, compared with the traditional Markov chain, the transition probability of the spatial Markov chain is reduced due to the influence of surrounding cities. Thus, policies are given.

Resilience of regional container port network: based on projection correlation and dynamic spatial Markov Chain

ABSTRACT Ports are interconnected by shipping routes within the global logistics network. Shocks and risks stemming from financial crises, epidemics, ecology, and so on have prompted shifts in cross-border labor division towards regional cooperation, reflecting an ‘anti-globalization’ trend. Despite these dynamics, few studies have analyzed the spatial structure and dynamic evolution of resilience within regional container port network. Addressing this gap, this study considers spatial and dynamic attributes to identify influential factors of resilience and introduces the concept of spatial lag-multidimensional resistance (SL-MR) to strengthen the understanding and cognition of regional container port network resilience (RCPNR). By ports in Tianjin and Hebei, China, it constructs a dynamic spatial Markov chain (DSMC) based on the projection correlation (PC index) to dissect transition trajectories amid financial crises and other conflicts. The findings indicate: (1) The regional container port network often experiences a low-level path dependence like ‘poverty trap.’ (2) Hierarchical spatial lags significantly influence the transition probability of container port network resilience degradation, with regional port relationships being unidirectional rather than cooperative. (3) The impact of the 2008 financial crisis and the COVID-19 pandemic on regional port resilience varies, serving as key determinants in shaping diverse resilience types.

A Spatiotemporal Evolution and Pathway Analysis of Rural Development Efficiency: A Case Study of the Yangtze River Delta

Sustainable rural development is crucial for urban–rural integration and achieving shared prosperity. This study assesses rural development efficiency (RDE) at the county level in the Yangtze River Delta (YRD) region from 2012 to 2021 using the super-slacks-based measure model (SBM). By employing the Theil index and spatial Markov chains, this study explores the spatiotemporal evolution of RDE, categorizes rural development types, and proposes differentiated developmental pathways. The findings reveal (1) an initial upward trend in overall RDE in the YRD followed by stabilization, with regional disparities narrowing yet overall efficiency levels remaining relatively low; (2) a spatial distribution pattern of RDE characterized by high efficiency in the southeast and low efficiency in the northwest, forming a “core–periphery” structure, with pure technical efficiency identified as a significant bottleneck; (3) stability and “club convergence” in RDE, with geographic spatial patterns significantly influencing the transition process and a notable spatial spillover effect; (4) the classification of rural development types into six categories based on an “input–output–efficiency” perspective, each with specific developmental pathways. This study concludes that optimizing resource allocation efficiency and defining development pathways tailored to local conditions are essential for driving sustainable rural development in the YRD.

Assessing green production efficiency and spatial characteristics of China’s real estate industry based on the undesirable super-SBM model

As China strives to balance rapid urbanization with environmental conservation, increasing attention is being paid to the pursuit of green production efficiency (GPE) in the real estate industry. The undesirable super-SBM model was used to calculate the GPE of China's real estate industry from 2001 to 2020. Additionally, GPE spatial distribution characteristics in China's real estate industry were analyzed using the standard deviation ellipse (SDE), Moran’s index, Theil index, random kernel density estimation (RKDA), and spatial Markov chain (SMC) methods. The GPE exhibited a U-shaped trend, with 2008 as the inflection point, first decreasing and then increasing. It reached a maximum value of 0.747 in 2020. The Theil index increased from 0.043 to 0.121 nationwide, indicating the overall characteristics of low-level slow growth, and imbalance. Discrepancies in input–output scales, the southward shift of economic centers, and population movements contribute significantly to the disparities between the east and west, north and south, and regions divided by the Hu Huanyong Line (Hu Line). The GPE exhibited club convergence characteristics; however, polarization phenomena exist in local areas. Spatial spillover effects were also observed in GPE. Finally, we provide recommendations for promoting green development in the real estate industry, including green building technology, fiscal subsidy investment, and population migration management.

Spatial Evolution and Driving Factors of Ecological Well-Being Performance in the Yellow River Basin

Ecological well-being performance (EWP) is a key indicator of sustainable development and has garnered significant research attention. This study measures the overall and stage-by-stage efficiency of the urban agglomerations in the Yellow River Basin based on the ends–means framework of steady-state economics. This study then delves into the spatiotemporal transfer characteristics of EWP through Moran’s I, and spatial Markov chains. Additionally, this research investigates the factors influencing EWP using a random forest model. The findings indicate a notable enhancement in EWP in the urban agglomerations in the YRB from 2006 to 2021, showing clear spatial agglomeration patterns. The shift in EWP types displays a “path dependence” effect, with distinct evolutionary paths influenced by spatial lag effects. Ecological input emerges as a key internal driver of EWP, while urbanization and technological advancements are highlighted as significant external factors. Industrial agglomeration and industrial structure also contribute to improving EWP. The findings of this study help to clarify the spatial and temporal characteristics of ecological welfare performance and its driving mechanisms in the urban agglomerations of the Yellow River Basin. This is conducive to the achievement of high-quality urban transformation and regional green development, and it provides a reference for the construction of an ecological civilization.

Mapping benthic sediment types and composition in a turbid Jamaican bay using hydroacoustic data and different spatially explicit interpolation techniques

Hydroacoustic sensors are better suited for mapping shallow turbid coastal waters, but surveys must take place along transects, necessitating the interpolation of processed, large, spatially dependent hydroacoustic datasets to produce continuous maps. The aim of this study was to evaluate the performance of interpolation techniques that can handle data obtained from a hydroacoustic survey (covering 23 km2) conducted in 2016 in a shallow bay in southwestern Jamaica. Independent data or a 5-fold cross validation were used to compare the accuracies of continuous maps of benthic sediment percentage composition and submerged aquatic vegetation cover (SAV), interpolated from the survey data using random forest (RF) regression (RFr), RFr kriging (RFrk) and Ensemble Machine Learning (EML) with rotated/oblique coordinates. Categorical/thematic maps of sediment types interpolated using RF (RFc) and EML classifiers and a spatial Markov chain model with indicator kriging predictor/simulation (Mc-IK) were also compared. RFr, RFc, RFrk and EML covariates included coordinates and auxiliary data. The following maps were more accurate than corresponding interpolated maps: EML - mud, sand, silt, clay and SAV; RFr - loss on ignition [LOI]; RFrk - calcium carbonate; Mc-IK – sediment types with 3 and 4 classes. The Mc-IK maps were also the most suitable maps for assessing spatial and quantitative changes. The predominant substrate type in the bay was muddy sand covering 15.7 km2. The highest percentage composition of mud, silt, clay, and organic matter [LOI] was found close to the mouth of the Black River where sandy mud was found, indicating a high input of terrigenous sediment. Calcium carbonate composition showed an opposite spatial trend and was positively associated with higher SAV percentage cover. The bay is now part of a protected area, as such, these maps will be used as a baseline to gauge the impact of increased protection/management on benthic dynamics.

Spatio-Temporal Characteristics,Regional Differences and Dynamic Evolution of Green Finance Development Level in China

As an emerging field that integrates finance and environmental protection, green finance aims to promote sustainable development and environmental protection through financial instruments and market mechanisms. The Chinese green finance market is rapidly emerging as a significant force in the global green finance arena. This paper applies methods such as entropy weight method, Dagum Gini coefficient, kernel density estimation, and spatial Markov chain to analyze the spatiotemporal characteristics, regional differences, and dynamic evolution of China’s green finance development level from 2010 to 2021. The results of the study indicate that the comprehensive index of China’s green finance development level has been increasing, with the mean comprehensive index of green finance development level showing a pattern of "East >National>Central>West". The overall level of green finance development in Chinese provinces has been increasing as a whole, and the level of some provinces has been moving upwards. The regional differences in green finance development level have been decreasing over the study period, with the central region having the smallest regional differences and the eastern region having the largest. From the perspective of distribution dynamics, it can be seen that the degree of imbalance in the green finance regional development in China is gradually increasing, with polarization phenomenon existing. The green finance development of Chinese provinces does not exist in isolation, and there is a certain degree of interdependence among them. When the green finance development level of neighboring provinces is better than that of the local area, it is conducive to the improvement of the local green finance development level, and there is the possibility of leapfrogging transfer.

Spatial Analysis and Prediction of Ecological Welfare Performance: Evidence from 284 Prefecture-level Cities in China

The general debate of the 78th session of the United Nations reaffirmed the importance of the Sustainable Urban and Community Development Goal (SDG 11), however, the pervasive accounting gaps still lead to significant challenges in the monitoring and evaluation of SDG 11 indicators. This paper focuses on ecological welfare performance accounting, aiming to assess the weaknesses and internal gaps of regional SDG 11 at the aggregate level, so as to promote eco-city planning and management, improve the well-being potential of urban residents, and reduce the adverse impact on the ecological environment. Taking China as a developing country as a case, the two-stage DEA model is used to measure the ecological welfare performance of 284 cities in China from 2007 to 2022, and the spatiotemporal pattern of efficiency at each stage of the ecological welfare transformation process is described, a modified gravitational model is constructed to transform the "attribute data" of ecological welfare performance into "relationship data", and the characteristics of network structure are described with the help of social network analysis, and the formation mechanism of the spatial correlation network of ecological welfare performance is revealed and its dynamic evolution characteristics are revealed through the spatial Markov chain.

Regional differences, dynamic evolution, and obstacle factors of cultivated land ecological security in China

Under the influence of geopolitical conflicts, extreme weather, and other factors, global food security has been severely impacted. Cultivated land ecological security in China is vital for guaranteeing local food security and sustainable development. Therefore, using provincial data from 2003 to 2020, this research establishes an index system based on the pressure-state-response (PSR) model and assesses its cultivated land ecological security via the entropy weight method. A series of measurements such as Dagum's Gini coefficient and spatial Markov chain are then adopted to reveal changes in regional differences and spatial-temporal characteristics of cultivated land ecological security in China. The findings indicate that this ecological security has risen from insecure to critically secure, but the overall gap has gradually widened, mainly stemming from interregional differences. Cultivated land ecological security exhibits positive spatial clustering characteristics and the phenomenon of club convergence. The amount of water resources, the degree of soil erosion control, and the strength of local financial expenditure on agriculture are the main factors hindering its cultivated land ecological security, and the threats mainly come from the response criterion layer. Therefore, it is essential to strengthen measures for cropland ecological protection, to formulate targeted strategies by combining the regional characteristics, to narrow the intra-regional gap, and to bring into play the spatial radiation effect of high-level provinces.

Spatio-temporal evolution and driving factors of green innovation efficiency in the Chinese urban tourism industry based on spatial Markov chain

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.

Analysis of the Current Situation of Sustainable Development of the Yangtze River Economic Belt based on SDGs

This paper evaluates the level of sustainable development of 11 provinces and cities in the Yangtze River Economic Belt from 2011 to 2020, and analyzes its spatial and temporal differentiation and trend evolution characteristics. This paper adopts the coupling coordination model based on CRITIC-entropy weight combination weight, spatial autocorrelation model, spatial Markov chain and other methods, based on the measurement indicators of the United Nations Sustainable Development Goals, and conducts a comprehensive evaluation from four aspects: innovation-driven, welfare guarantee, resources and environment, and economic development. The research results show that the sustainable development level of the Yangtze River Economic Belt presents the characteristics of continuous improvement, regional difference, hierarchical leap, spatial dependence, spillover effect, etc. According to the research findings, this paper puts forward some suggestions, such as strengthening regional coordinated development, optimizing spatial planning and governance, coordinating policies and work, promoting rule of law and innovation, etc., aiming to promote the high-quality development of the Yangtze River Economic Belt, achieve the coordinated development of economy, society and ecology, and localize the SDGs. This paper’s research has important theoretical significance and practical value for deeply understanding the current situation and problems of sustainable development of the Yangtze River Economic Belt.

Towards sustainability: The spatiotemporal patterns and influence mechanism of urban sprawl intensity in the Yangtze River Delta urban agglomeration

Urban sprawl has been a great challenge to current sustainable development goals, especially in urban agglomeration areas. Based on the nighttime light and WorldPop population grid data from 2011 to 2020, this study established a multidimensional evaluation system of urban sprawl intensity in the Yangtze River Delta Urban Agglomeration (YRDUA) with a "scale-density-structure" framework. The Dagum Gini coefficient, spatial Markov chain, and econometric analysis were applied to analyze urban sprawl intensity. The results suggested that: (1) the urban sprawl intensities of the cities in the YRDUA became more imbalanced over the decade; (2) the mild sprawl cities were dominant in the study area, but the moderate and severe sprawl cities were mostly located in the west and the only inland province--Anhui; (3) the spatial distribution of urban sprawl intensity indicated "club convergence" and strong spatial correlation effect, but poor stability; and (4) land resource mismatch and population urbanization were the main explanatory variables for urban sprawl intensity, with large cities more sensitive to the former, and medium cities to the latter. These findings can provide important policy implications to sustainable development and compact growth.

Investigation into the Performance Characteristics of the Organic Dry Farming Transition and the Corresponding Impact on Carbon Emissions Reduction

Global warming affects food security and ecological security, and it threatens economic stability and sustainable agricultural development. The transformation and development of agriculture have significant implications for the achievement of the “dual-carbon” goals and the promotion of sustainable agricultural development. Based on panel data on organic dry farming in China from 2005 to 2020, this study aimed to comprehensively assess the transformation performance of organic dry farming (TRODF) in 15 provinces. It explored the impact of the transformation of organic dry farming on carbon emissions by utilizing a spatial Markov chain and spatial measurement models. Our findings are as follows: (1) The performance of the organic dryland agriculture transformation has gradually improved and is accompanied by a corresponding trend of fluctuating regional disparities, which are on the rise. Moreover, the disparities between the five major regions mainly stem from intra-regional differences. (2) TRODF agriculture presents the possibility of state transfer during different periods, featuring four convergent zones: a lagging zone, a starting zone, a crossing zone, and an advanced zone. The spatial Markov chain indicates that state transitions typically occur between adjacent levels, with fewer instances of “jump”-type transitions. Moreover, there is a clear trend of differentiation in the state transitions between non-adjacent areas. (3) The organic dry farming transformation exhibits a significant carbon reduction effect, which is characterized by heterogeneity across different stages of agricultural development, provinces, and time periods. This study emphasizes that economic and industrial transformation, along with the transformation of the ecological environment, represents a crucial direction for conserving resources and achieving a further reduction in carbon emissions.

Spatial differences, dynamic evolution and influencing factors of China's construction industry carbon emission efficiency

Improving the construction industry carbon emission efficiency (CICEE) is crucial for achieving sustainable development. To promote low-carbon development in the construction industry, it is essential to measure carbon emission efficiency (CEE) and analyze spatial differences, dynamic evolution, and influencing factors. This study measures CICEE in 30 provinces in China from 2005 to 2019 and evaluates CEE using the minimum distance to a strong efficient frontier (MinDS) model with undesirable outputs. Subsequently, the Dagum Gini coefficient and its decomposition, as well as spatial autocorrelation analysis, are used to explore the sources of spatial differences and the spatial clustering pattern of CEE. The dynamic trend of CEE is analyzed through kernel density estimation, traditional and spatial Markov chains. Finally, geographical detectors are used to detect the explanatory factors and their interactions on spatial differences in CEE. The results of this study show that the CICEE presents an increasing and then decreasing trend, with the highest CEE in the eastern region, followed by the central and northeastern regions, and the lowest in the western region. Additionally, the eastern region exhibits the highest intra-regional differences and the highest inter-regional differences with the western region. Meanwhile, CEE shows a positive spatial correlation, with high-high (H-H) clustering in the eastern region and low-low (L-L) clustering in the western and northeastern regions. Polarization has been evident throughout the entire country and its four regions in recent years. It is challenging to achieve the CEE transfer through rapid advancement, and the efficiency of neighboring provinces will influence the potential transfer of the local province. Finally, factors such as enterprise scale, economic development level, degree of openness to the outside world, innovation level, industrial structure, and energy consumption structure all affect the spatial differences in CEE, with the interaction effect being higher than the single factor. This study presents a novel computational model to measure CICEE, analyzes the structural factors contributing to the spatial differences in CICEE, and provides theoretical support for the synergistic improvement of CEE across different regions. Combining with spatial autocorrelation analysis, the spatial distribution characteristics of CICEE are analyzed from the static level. This study provides a comprehensive examination of the evolution trend of CICEE, focusing on its dynamic evolution characteristics and the long-term transfer dimension. Additionally, geographical detector technology is introduced for the first time to analyze the influencing factors of spatial differences in CICEE. providing scientific evidence for the sustainable and coordinated development of different regions in China's construction industry. Furthermore, this study also contributes to the development of varied strategies for improving CICEE in China.