Analysis Of Spatial Dynamics Research Articles

Fintech’s role in carbon emission efficiency: dynamic spatial analysis

Against the backdrop of digitization and global warming, fintech plays a crucial role in accelerating the growth of green finance, driving innovation in the financial industry, and catalyzing the low-carbon transformation of economic activities. This paper utilizes city panel data from 2007 to 2019 to examine the direct impact of fintech on carbon emission efficiency (CEE), the transmission channels of green technological innovation and green finance, and the spatial spillover effects, using dynamic panel models, mediation effect models, and dynamic spatial Durbin models (SDM). The study finds that: (i) Fintech significantly improves CEE, and this conclusion remains robust after accounting for potential endogeneity issues and conducting robustness tests. (ii) Mechanism analysis reveals that green finance and green technological innovation are the primary channels through which fintech influences CEE. (iii) Results from the dynamic SDM model indicate that fintech has a significant positive spatial spillover effect on CEE, with the long-term spillover effect being smaller than the short-term spillover effect. (iv) Heterogeneity analysis reveals that fintech’s improvement effect on CEE is mainly evident in eastern regions, emerging first-tier and first-tier cities, and non-resource-based cities. Our research provides new insights for policymakers on achieving China’s dual-carbon goals through the promotion of fintech development, green finance, and green technological innovation. It also aids in the coordinated development of fintech among cities and the formulation of differentiated policies, providing a theoretical foundation and empirical support for future research.

Dynamic in situ detection in iRhizo-Chip reveals diurnal fluctuations of Bacillus subtilis in the rhizosphere

Effective colonization by microbe in the rhizosphere is critical for establishing a beneficial symbiotic relationship with the host plant. Bacillus subtilis, a soil-dwelling bacterium that is commonly found in association with plants and their rhizosphere, has garnered interest for its potential to enhance plant growth, suppress pathogens, and contribute to sustainable agricultural practices. However, research on the dynamic distribution of B. subtilis within the rhizosphere and its interaction mechanisms with plant roots remains insufficient due to limitations in existing in situ detection methodologies. To achieve dynamic in situ detection of the rhizosphere environment, we established iRhizo-Chip, a microfluidics-based platform. Using this device to investigate microbial behavior within the rhizosphere, we found obvious diurnal fluctuations in the growth of B. subtilis in the rhizosphere. Temporal dynamic analysis of rhizosphere dissolved oxygen (DO), pH, dissolved organic carbon, and reactive oxygen species showed that diurnal fluctuations in the growth of B. subtilis are potentially related to a variety of environmental factors. Spatial dynamic analysis also showed that the spatial distribution changes of B. subtilis and DO and pH were similar. Subsequently, through in vitro control experiments, we proved that rhizosphere DO and pH are the main driving forces for diurnal fluctuations in the growth of B. subtilis. Our results show that the growth of B. subtilis is driven by rhizosphere DO and pH, resulting in diurnal fluctuations, and iRhizo-Chip is a valuable tool for studying plant rhizosphere dynamics.

Coupling Hyperbolic GCN with Graph Generation for Spatial Community Detection and Dynamic Evolution Analysis

Spatial community detection is a method that divides geographic spaces into several sub-regions based on spatial interactions, reflecting the regional spatial structure against the background of human mobility. In recent years, spatial community detection has attracted extensive research in the field of geographic information science. However, mining the community structures and their evolutionary patterns from spatial interaction data remains challenging. Most existing methods for spatial community detection rely on representing spatial interaction networks in Euclidean space, which results in significant distortion when modeling spatial interaction networks; since spatial community detection has no ground truth, this results in the detection and evaluation of communities being difficult. Furthermore, most methods usually ignore the dynamics of these spatial interaction networks, resulting in the dynamic evolution of spatial communities not being discussed in depth. Therefore, this study proposes a framework for community detection and evolutionary analysis for spatial interaction networks. Specifically, we construct a spatial interaction network based on network science theory, where geographic units serve as nodes and interaction relationships serve as edges. In order to fully learn the structural features of the spatial interaction network, we introduce a hyperbolic graph convolution module in the community detection phase to learn the spatial and non-spatial attributes of the spatial interaction network, obtain vector representations of the nodes, and optimize them based on a graph generation model to achieve the final community detection results. Considering the dynamics of spatial interactions, we analyze the evolution of the spatial community over time. Finally, using taxi trajectory data as an example, we conduct relevant experiments within the fifth ring road of Beijing. The empirical results validate the community detection capabilities of the proposed method, which can effectively describe the dynamic spatial structure of cities based on human mobility and provide an effective analytical method for urban spatial planning.

Analysis of temporal and spatial dynamics and driving factors in the aquaculture industry of Fuding City, China

BackgroundThe sustainable development of aquaculture is a crucial determinant of food security, the well-being of aquaculture practitioners, and economic growth within coastal regions. Considering the existing gap in research regarding spatial and temporal development of aquaculture, this study investigates the progression of aquaculture practices over time and across various locations in Fuding City, China. MethodsWe retrospectively collected temporal and spatial data on aquaculture, as well as demographic, social, and economic data for Fuding City from 2010 to 2020. By employing 3D kernel density analysis, we illustrated the temporal and spatial changes in aquaculture. Furthermore, we utilized Ordinary Least Squares regression to investigate the driving factors behind the spatial changes in the aquaculture industry. ResultsOver the past decades, we observed that in Fuding City, both the number of fishing rafts and aquaculture households initially decreased and then increased. The spatial distribution of aquaculture experienced a shift from the west (inner bay area) to the east (coastal area). Additionally, the type of fishing rafts also varied by region, with traditional rafts dominating the western inner bay and plastic rafts prevalent in the eastern offshore areas. Analysis of driving factors revealed that at least six factors have a significant positive correlation with the eastward shift of the aquaculture industry's center, including the proportion of migrant population, proportion of aquaculture to total fishery output, average temperature, investment in aquaculture technology, total fish sales, and GDP of Fuding City. ConclusionThis study examines the spatial and temporal dynamics of aquaculture in Fuding City from 2010 to 2020, proposing an innovative approach to spatial optimization that integrates both horizontal and vertical strategies. These insights aim to guide the development of coastal aquaculture policies and support sustainable regional development, fostering a balanced coexistence between human activities and marine environments.

Delineating the mechanisms controlling groundwater salinization using chemo-isotopic data and meta-heuristic clustering algorithms (case study: Saguenay-Lac-Saint-Jean region in the Canadian Shield, Quebec, Canada).

This study employed meta-heuristic clustering algorithms to determine the source and mechanism of groundwater salinization in Quebec's Saguenay-Lac-Saint-Jean (SLSJ) region, utilizing hydrogeochemical (38 inorganic constituents, including minor, major, and trace elements) and isotopic data (δ18O and δ2H). A total of 382 groundwater and precipitation samples were examined. Among the meta-heuristic algorithms, Artificial Bee Colony K-Means (ABCKM), Differential Evolution K-Means (DEKM), Harmony Search K-Means (HSKM), Particle Swarm Optimization K-Means (PSOKM), and Genetic K-Means (GKM) were used and investigated, and finally, PSOKM displayed superior performance and was chosen for further investigation. Analysis of diverse plots and hydrogeochemical modeling unveiled the impact of the Laflamme Sea invasion on groundwater chemistry. PSOKM1, PSOKM4, and PSOKM5 exhibited notable carbonate and silicate dissolution, with PSOKM4 demonstrating predominant carbonate dissolution. Cation exchange was identified through binary plots and Chloro Alkaline Index (CAI), with reverse cation exchange predominantly observed in most PSOKM4 samples, while positive values suggested direct cation exchange in other clusters. Spatial dynamics analysis using HFE-D indicated that salinization occurs as groundwater flows through crystalline bedrock aquifers, resulting in a transition from HCO3- dominance in PSOKM4 to Cl- dominance in the remaining clusters. Interaction between groundwater and rock along this path facilitated a transformation towards a Na-Cl end-member. The closely aligned stable isotopes with the Global Meteoric Water Line (GMWL) indicated a blend of meteoric water and seawater as the groundwater source.

Analysis of Spatial and Temporal Dynamics of Finland’s Boreal Forests and Types over the Past Four Decades

In the context of global warming, the study of the long-term spatial change characteristics of boreal forest cover is not only important for global climate change and sustainable development research but can also provide support for further research on the response of boreal forest changes to climate change. Using Landsat TM/OLI images from 1980 to 2020 as the data source and Google Earth Engine (GEE) as the platform, Finland was selected as the study area of boreal forests, and typical sample points of different features were chosen to classify forested and non-forested land using the random forest algorithm combined with spectral indices and classified feature sets of tasseled cap transform to obtain the four-phase forest cover change maps of the region. GEE test sample points and random selection points of images from the GF-2 and GF-7 satellites were used for verification. The classification accuracy was 97.17% and 88.9%. The five-phase forest cover images were segmented by a 2° latitude zone, and the spatial and temporal dynamic changes in forest cover in the whole area and each latitude zone were quantified by pixel superposition analysis. The results showed that, in the past 40 years, the boreal forest cover in Finland changed significantly, and the forest cover decreased from 75.79% to 65.36%, by 10.43%. Forest change mainly occurs in coniferous forests, whereas broadleaf forests are more stable. The forest coverage in each latitude zone decreased to varying degrees, with higher changes occurring in high-latitude areas above 64° N between 1980 and 2000, and higher and more severe changes occurring in low-latitude areas below 64° N between 2000 and 2020. Coniferous forests are the dominant type of forest in Finland, and the degradation of coniferous forests in the south is likely to become more severe, whereas the north and above is likely to become more favorable for coniferous forests. More monitoring and research are needed to follow up on the very different changes in the north and south regions.

Boundaries of Privacy: A Reevaluation of Spatial Dynamics in Traditional Kerala Homes

Privacy within architectural spaces is a multifaceted phenomenon deeply intertwined with cultural norms and social practices. This paper explores the boundaries of privacy in traditional Kerala homes, characterized by open-plan layouts and communal living practices. Through a meticulous analysis of spatial dynamics in Thazhathangady, Kalpathy Agraharam, and East Fort Agraharam, the research uncovers the intricate relationship between spatial organization, gender roles, and privacy considerations. The findings reveal clear demarcations between public, semi-public, and private domains within these homes, reflecting cultural norms and social expectations. Gender significantly influences space utilization, with women primarily occupying areas associated with domestic tasks, while men frequent spaces designated for communal activities. The adaptability and multifunctionality of spaces, particularly courtyards, emerge as central elements in traditional Kerala architecture, accommodating various activities while maintaining privacy. Regional variations further highlight the nuanced approach to privacy, tailored to local customs and traditions. This study contributes to a deeper understanding of privacy dynamics within traditional Kerala architecture, informing culturally sensitive approaches to architectural design and urban planning. Keywords: Privacy, Traditional Kerala Architecture, Spatial Dynamics, Communal Living, Gender Roles, Cultural Norms, Spatial Organization, Multifunctionality, Regional Variations, Architectural Heritage.

Layer chicken microbiota: a comprehensive analysis of spatial and temporal dynamics across all major gut sections

BackgroundThe gut microbiota influences chicken health, welfare, and productivity. A diverse and balanced microbiota has been associated with improved growth, efficient feed utilisation, a well-developed immune system, disease resistance, and stress tolerance in chickens. Previous studies on chicken gut microbiota have predominantly focused on broiler chickens and have usually been limited to one or two sections of the digestive system, under controlled research environments, and often sampled at a single time point. To extend these studies, this investigation examined the microbiota of commercially raised layer chickens across all major gut sections of the digestive system and with regular sampling from rearing to the end of production at 80 weeks. The aim was to build a detailed picture of microbiota development across the entire digestive system of layer chickens and study spatial and temporal dynamics.ResultsThe taxonomic composition of gut microbiota differed significantly between birds in the rearing and production stages, indicating a shift after laying onset. Similar microbiota compositions were observed between proventriculus and gizzard, as well as between jejunum and ileum, likely due to their anatomical proximity. Lactobacillus dominated the upper gut in pullets and the lower gut in older birds. The oesophagus had a high proportion of Proteobacteria, including opportunistic pathogens such as Gallibacterium. Relative abundance of Gallibacterium increased after peak production in multiple gut sections. Aeriscardovia was enriched in the late-lay phase compared to younger birds in multiple gut sections. Age influenced microbial richness and diversity in different organs. The upper gut showed decreased diversity over time, possibly influenced by dietary changes, while the lower gut, specifically cecum and colon, displayed increased richness as birds matured. However, age-related changes were inconsistent across all organs, suggesting the influence of organ-specific factors in microbiota maturation.ConclusionAddressing a gap in previous research, this study explored the microbiota across all major gut sections and tracked their dynamics from rearing to the end of the production cycle in commercially raised layer chickens. This study provides a comprehensive understanding of microbiota structure and development which help to develop targeted strategies to optimise gut health and overall productivity in poultry production.

Spatial dynamic analysis and thematic mapping of vulnerable communities to urban floods

Natural disasters, particularly floods, have increasingly impacted urban areas globally. Previous research has not adequately analyzed the vulnerability to floods of populations based on race and ethnicity. This study aims to examine demographic changes over the past two decades in five southern US cities and determine whether minority and non-minority populations have moved closer to or farther from flood-prone areas. Using a spatial clustering approach, the study identifies geographical clusters of the white and minority populations within the 100-year floodplain. In general, the white people are moving away from floodplain areas in Birmingham and Jackson. Except for Charleston, the highest populations clustered in floodplains were minority communities (the African American and Hispanic). In Birmingham, both the White and the African American populations in floodplains have decreased due to the federal and local governments' relocating populations through acquisition projects. The African American in Jackson, Birmingham, and New Orleans were particularly vulnerable to flooding. Houston and New Orleans had also significant increases in the Hispanic and Asian populations within floodplains. The findings can help federal and local governments and communities understand spatial and temporal pattern dynamics between minorities and the White, which assists for effective decision making and equitable solutions to vulnerable communities.

Historic conservation in Tibetan region amidst rapid urbanization: a case study of Dukezong Old Town

ABSTRACTRapid economic growth in China has resulted in the revitalization of historic Chinese ethnic towns, impacting their spatial and renowned integrity. This study focuses on Dukezong Old Town (Domkhar Rdzong), a traditional Tibetan settlement in the southernmost Tibetan cultural region, to examine its historical revolutions under modern urban renewal. The study begins by highlighting the historic significance of the “Rdzong” to establish the core essence of Dukezong as a Tibetan traditional Rdzong-Shan settlement. Furthermore, spatial-dynamic analysis was conducted to demonstrate how the urban construction process has contributed to the diminishing presence of “Rdzong.” The findings indicate that urban renewal efforts in Dukezong have accelerated the deterioration of its historic urban form and diminished its original residential function. Drawing from Pre-existing Theory, strategies are proposed that involve preserving Rdzong’s traditional space without physical objects and sustaining Rdzong’s residential function to reinvigorate Dukezong’s distinctive cultural heritage. This study emphasizes the significance of recognizing a region’s historic significance, functions, and urban morphology in revitalizing historic towns inhabited by ethnic minorities. Moreover, it offers valuable insights into the relationship between urban preservation and renewal in other historic towns.

Analysis of Temporal and Spatial Dynamics of Ecosystem Services and Trade-Offs/Synergies during Urbanization in the Loess Plateau, China

As a typical ecological fragile zone and an area with a high intensity of human activities, the Loess Plateau (LP) of China has significantly altered its ecosystem and the corresponding services under the influence of urbanization processes. However, most existing studies focus on the spatial and temporal changes of ecosystem services (ESs) and their interrelationships under the influence of ecological restoration works in the LP, leaving limited research on the impacts of urbanization on ESs. Therefore, this study constructed a research framework for exploring the spatio-temporal dynamics and interactions of ESs under the influence of urbanization based on time series data from 2000 to 2020. The results showed that: (1) based on the comprehensive urbanization level (CUL), developed and developing areas accounted for 5.63% of the total area; (2) for the whole LP, all ESs except Habitat Quality (HQ) showed an increased trend. HQ showed a trade-off with the other services, while there was a clear synergy between the other three types of services; (3) in terms of processes of urbanization, Carbon Sequestration, Water Yield and HQ gradually decreased with increased levels of urbanization, and Soil Conservation increased the least in developing areas. The trade-off between HQ and the other three services decreased with increasing urbanization, while the synergy between the other three services strengthened as urbanization deepened. These findings suggest that urbanization significantly impacts ESs. It is necessary to implement appropriate measures (e.g., sponge city construction, urban green space, etc.) to address the impacts of urbanization on ESs.

Unveiling the Surge: Exploring Elevated Air Pollution Amidst the COVID-19 Era (2019–2020) through Spatial Dynamics and Temporal Analysis in Delhi

Unveiling the Surge: Exploring Elevated Air Pollution Amidst the COVID-19 Era (2019–2020) through Spatial Dynamics and Temporal Analysis in Delhi

Spatial analysis of vaccine coverage in children under the age of 1 year by mesoregions in Paraíba a northeastern Brazilian state.

Immunization is one of the most effective measures in public health, and it is responsible for the reduction of vaccine-preventable diseases. In the present study, vaccine coverage (VC) and the spatial dynamics of homogeneity of VC (HVC) were compared and analyzed in the terms of the immunobiologicals administered to children aged < 1 year in a state in Paraíba, Brazil. This is a mixed ecological study that used public-domain secondary data from the years 2016 and 2017 from the Information System of the Brazilian National Immunization Program (SI-PNI) and the Brazilian National Information System of Live Births (SINASC). VC rates were calculated by dividing the number of administered doses by the number of live births. Then, VC was classified into four categories. The Municipal HVC was considered adequate when the overall VC exceeds 75%. The study included a descriptive analysis and a spatial autocorrelation analysis for HVC using global and local Moran's statistics. The stratified VC analysis revealed a significant number of municipalities in each of the state's mesoregions with low or very low VC rates for all immunobiologicals, with the Mata Paraibana mesoregion having the worst percentages in both years studied. The spatial analysis of HVC revealed several clusters of inadequate homogeneity, with Mata Paraibana being the worst mesoregion in 2016. The analysis of spatial dynamics and spatial statistics techniques allows the precise identification of vulnerable areas, "vaccination pockets," making it possible to develop plans aimed at meeting the targets of the PNI.

Statistical Investigation of Rotary Fluidized Bed Agglomeration Process with Tangential Spray and In-Line Particle Size Measurement for PAT Process Control

A statistical design of experiments for a rotary fluidized bed agglomeration process is performed to improve both the knowledge of the process and the influence of the process parameters. Agglomerates of a pharmaceutical formulation are manufactured in a laboratory fluidized bed rotor apparatus with a tangential spray nozzle. Particle size is measured in-line over the entire agglomeration process with a spatial filter velocimetry probe installed directly in the process chamber and off-line with dynamic image analysis for comparison. The influence of the process parameters spray rate, spray pressure, rotor speed, and process air temperature on the fluidized bed is investigated using a central composite design. In-line measurement of particle size is possible over the entire rotor process. Spray pressure, spray rate, square of process air temperature, and some interactions proved to be statistically significant. Particle size measured with spatial filter velocimetry and dynamic image analysis indicates good agreement and a similar trend. The successful application of particle size measurement in a fluidized bed rotor agglomeration at a laboratory scale using spatial filter velocimetry to improve process control and reduce the risk of failed batches serves as the basis for transferring to a production scale.

Retracted: Analysis of Passenger Flow Characteristics of Urban Rail Transit Based on Spatial Data Dynamic Analysis Technology

Retracted: Analysis of Passenger Flow Characteristics of Urban Rail Transit Based on Spatial Data Dynamic Analysis Technology

Spatial dynamic analysis for COVID-19 epidemic model with diffusion and Beddington-DeAngelis type incidence

&lt;p style='text-indent:20px;'&gt;A diffusion SEIAR model with Beddington-DeAngelis type incidence is proposed to characterize the spread of COVID-19 with spatial transmission. First, the well-posedness of solution is studied. Second, the basic reproduction number &lt;inline-formula&gt;&lt;tex-math id="M3"&gt;\begin{document}$ \mathcal R_{0} $\end{document}&lt;/tex-math&gt;&lt;/inline-formula&gt; is derived and served as a threshold value to determine whether COVID-19 will spread. Meanwhile, we consider the effect of diffusion on the spread of COVID-19 in spatial homogenous environment, by which we can obtain that if &lt;inline-formula&gt;&lt;tex-math id="M4"&gt;\begin{document}$ \mathcal R_{0}&amp;lt;1 $\end{document}&lt;/tex-math&gt;&lt;/inline-formula&gt;, then the infection-free steady state is globally asymptotically stable, while if &lt;inline-formula&gt;&lt;tex-math id="M5"&gt;\begin{document}$ \mathcal R_{0}&amp;gt;1 $\end{document}&lt;/tex-math&gt;&lt;/inline-formula&gt;, then the endemic steady state is globally asymptotically stable. Furthermore, according to the official reporting data about COVID-19 in Wuhan, China, the actual value of &lt;inline-formula&gt;&lt;tex-math id="M6"&gt;\begin{document}$ \mathcal R_{0} $\end{document}&lt;/tex-math&gt;&lt;/inline-formula&gt; is estimated, and comparing with other types of incidence, we find that the estimated peak with Beddington-DeAngelis type incidence is more close to the cases in reality. Finally, by numerical simulations, we can see that the diffusion behavior has evident impact on the spread of COVID-19 in spatial heterogeneity than homogeneity of environment.&lt;/p&gt;

Spatial Dynamics and Predictive Analysis of Vegetation Cover in the Ouémé River Delta in Benin (West Africa)

The vegetation cover of the Ouémé Delta constitutes a biodiversity hotspot for the wetlands in southern Benin. However, the overexploitation of natural resources in addition to the intensification of agricultural practices led to the degradation of the natural ecosystems in this region. The present work aims to reconstruct, using remote sensing, the spatial dynamics of land use in the Ouémé Delta in order to assess the recent changes and predict the trends in its vegetation cover. The methodology was based on remote sensing and GIS techniques. Altogether, this process helped us carry out the classification of Landsat images for a period of 30 years (stating year 1990, 2005, and 2020) via the Envi software. The spatial statistics resulting from this processing were combined using ArcGIS software to establish the transition matrices in order to monitor the conversion rates of the land cover classes obtained. Then, the prediction of the plant landscape by the year 2035 was performed using the “Land Change Modeler” extension available under IDRISI. The results showed seven (07) classes of occupation and land use. There were agglomerations, mosaics of fields and fallow land, water bodies, dense forests, gallery forests, swamp forests, and shrubby wooded savannahs. The observation of the vegetation cover over the period of 15 years from 1990 to 2005 showed a decrease from 71.55% to 63.42% in the surface area of the Ouémé Delta. A similar trend was noticed from 2005 to 2020 when it reached 55.19%, entailing a loss of 16.37% of the surface area of natural habitats in 30 years. The two drivers of such changes are the fertility of alluvial soils for agriculture along and urbanization. The predictive modeling developed for 2035 reveals a slight increase in the area of dense forests and shrubby wooded savannas, contrary to the lack of significant decrease in the area of gallery forests and swamp forests. This is key information that is expected to be useful to both policy and decision makers involved in the sustainable management and conservation of natural resources in the study area.

Spatial Dynamic Analysis of Changes in Land Use Applying Markov Chain and Cellular Automata

Changes in land use in the Alo Pohu watershed have concerned the government in preventing the siltation of Limboto Lake, along with floods and slides. On that ground, the present study was conducted to analyze the spatial prediction of land-use changes in the Alo Pohu watershed, Gorontalo Regency, Indonesia, in the 2000-2017 period for the 2030 land use prediction. The method and analysis of spatial data to predict land-use changes involved the Powersim Version 10 and Idrisi apps. Further, the data consisted of two maps, i.e., the Indonesian topographical map and land use map. The Markov chain model applied to predict the land in the site area for the year of 2030 has an excellent suitability/agreement. Based on the validation test with the Kappa Index of Agreement, the value of K-standard gets 0.8 from 0-1 scale. Another validation using Google Earth is also employed, in which it shows an 83% of suitability level. Confirmed by the integration of the dynamic system simulation model, the map of land use resulting from the modeling is therefore scientifically acceptable. The study concluded that land conversion in the Alo Pohu watershed in Gorontalo Province has occurred.

Verification of PCSWMM's LID processes and their scalability over time and space

IntroductionThis paper explores the scalability of PCSWMM's Low Impact Development (LID) modeling tools within the urban stormwater computer model.MethodsThe scalability is assessed for a variety of spatial and temporal scales and for event (50-year return storm) and continuous inputs (daily rainfall for an 11 month period), and with a focus on bioretention cells. The model is calibrated for a moderate to large scale, semi-urban watershed on Vancouver Island, British Columbia, Canada. Sensitivity analysis and specialized metrics are used to verify internal model processes at a variety of scales.ResultsWith regard to spatial scaling, changes in flow path length and slope derived from Digital Elevation Models were the most impactful spatial information when modeling flood event and the model's surface layer was the dominant contributor to peak flowrate and volume mitigation by the bioretention cell. However, when modeling the continuous rainfall inputs, storage layer related parameters dominated model outputs. Aside from the soil layer's depth, soil layer parameters such as hydraulic conductivity, showed negligible influence on response to time series rainfall. Parameters that are kept static by the model such as vegetation cover, hydraulic conductivity and storage void ratio (but are naturally dynamic), were tested for their impact on response if allowed to change seasonally or with excessive loading. Runoff coefficients were greatly impacted by storage layer parameter dynamics with very little impact from vegetation. For event simulations, the berm height in the surface layer was the dominant player in reducing peak flow as well as total volume. An analysis to help illustrate sensitivity across spatial scales is proposed.DiscussionThe Spatial Dynamic Sensitivity Analysis shows that parameter sensitivity changes dynamically as LID implementation percentage changes. In particular, the clogging factor, which is a parameter associated with the storage layer, was highly influential for time series rainfall analysis. The LID model concepts in PCSWM seem appropriate for events because the surface layer dominates the response for very large storms. For smaller storms, continuous time series, and larger spatial scales, the model could be revised to better represent soil layer dynamics and vegetation cover, which were both currently inconsequential to the model's output.

Productivity growth convergence across Indian states: a spatial dynamic panel analysis

The study re-examines convergence among Indian states by estimating their total factor productivity (TFP) for the period 1990/91–2018/19. The novelty of the study is the decomposition of TFP change into efficiency change and technological change using the Malmquist index in a spatial dependence framework to test convergence. Findings reveal that, on average, productivity increased at an aggregate level for the entire period on account of the impressive growth of technological innovation. However, technological growth convergence could not lead to productivity growth convergence in the second sub-period because of no convergence in efficiency growth. Alongside, while we witness significant spatial spillovers in both technology and efficiency, convergence was observed only with respect to technology growth in the second sub-period. The congruence of technological convergence and significant spatial spillovers indicates that while states may be converging in technology, they do not do so independently but display movements similar to their regional neighbours.