Dynamic Spatial Model Research Articles

Fruit–frugivore dependencies are important in Ebolavirus outbreaks in Sub‐Saharan Africa

Ebolaviruses have the ability to infect a wide variety of species, with many African mammals potentially serving either as primary reservoirs or secondary amplifying hosts. Previous work has shown that frugivorous bats and primates are often associated with spillover and outbreaks. Yet the role that patterns of biodiversity, either of mammalian hosts or of common fruiting species such as Ficus (figs, fruit resources used by a wide variety of species), play in driving outbreak risk remains unclear. We investigated what factors most directly influence Ebolavirus outbreak risk in Sub‐Saharan Africa by using a phylogenetically informed path analysis to compare a wide array of potential models (path diagrams) of spatial dynamics. We considered mammalian frugivore richness, cercopithecid and hominid primate richness, richness of pteropodid (fruit) bats, the spatial distribution of species that have tested positive for Ebolavirus antibodies in the wild, Ficus habitat suitability, and environmental conditions (mean annual and variability in temperature and rainfall). The proximate factors that most influenced whether a given host species range contained a site of a previous outbreak event were 1) habitat suitability for Ficus and 2) the diversity of cercopithecid primates. Frugivore richness overall (including bats, primates, and a few other mammals) and the richness of bats in the family Pteropodidae had a strong effect on which species tested positive for Ebolavirus antibodies, but did not influence outbreak risk directly in pathways explored. We interpret this as evidence that foraging around Ficus and frugivorous mammals (such as cercopithecid primates which are commonly hunted for food) play a prominent role in driving outbreaks into human communities, relative to other factors we considered which influence outbreak risk more indirectly.

Fitting individual-based models of spatial population dynamics to long-term monitoring data.

Generating spatial predictions of species distribution is a central task for research and policy. Currently, correlative species distribution models (cSDMs) are among the most widely used tools for this purpose. However, a fundamental assumption of cSDMs, that species distributions are in equilibrium with their environment, is rarely fulfilled in real data and limits the applicability of cSDMs for dynamic projections. Process-based, dynamic SDMs (dSDMs) promise to overcome these limitations as they explicitly represent transient dynamics and enhance spatiotemporal transferability. Software tools for implementing dSDMs are becoming increasingly available, but their parameter estimation can be complex. Here, we test the feasibility of calibrating and validating a dSDM using long-term monitoring data of Swiss red kites (Milvus milvus). This population has shown strong increases in abundance and a progressive range expansion over the last decades, indicating a nonequilibrium situation. We construct an individual-based model using the RangeShiftR modeling platform and use Bayesian inference for model calibration. This allows the integration of heterogeneous data sources, such as parameter estimates from published literature and observational data from monitoring schemes, with a coherent assessment of parameter uncertainty. Our monitoring data encompass counts of breeding pairs at 267 sites across Switzerland over 22 years. We validate our model using a spatial-block cross-validation scheme and assess predictive performance with a rank-correlation coefficient. Our model showed very good predictive accuracy of spatial projections and represented well the observed population dynamics over the last two decades. Results suggest that reproductive success was a key factor driving the observed range expansion. According to our model, the Swiss red kite population fills large parts of its current range but has potential for further increases in density. We demonstrate the practicality of data integration and validation for dSDMs using RangeShiftR. This approach can improve predictive performance compared to cSDMs. The workflow presented here can be adopted for any population for which some prior knowledge on demographic and dispersal parameters as well as spatiotemporal observations of abundance or presence/absence are available. The fitted model provides improved quantitative insights into the ecology of a species, which can greatly aid conservation and management efforts.

Demand and distribution in a dynamic spatial panel model for the United States: Evidence from state‐level data

AbstractWe estimate a modified demand‐and‐distribution system for the 48 contiguous US states and the District of Columbia (DC) employing dynamic spatial panel data for 1980–2019. We allow for endogenous regressors, test for the presence, significance, and magnitude of spatial spillovers, and estimate immediate and cumulative effects on our endogenous variables of interest. Without testing for spatial dependence and spillovers, we estimate that output growth and capacity utilization in the sample US states and DC rise in response to an increase in their own wage share. When we test for spatial dependence and spillovers as required by the state‐level nature of the data, and consider that the functional distribution of income and the level of economic activity are jointly determined, we estimate that a higher state wage share raises output growth in the own state and the neighboring states. Yet the effect of a change in the state wage share on capacity utilization in the own state and the neighboring states is not statistically significant. Meanwhile, a higher state output growth raises the wage share in the own state, but its impact on the wage share in the neighboring states is not statistically significant. A higher state capacity utilization raises the wage share in the own state, yet it reduces the wage share in the neighboring states.

Research on Energy Conservation and Emission-Reduction Effects of Green Finance: Evidence from China

The energy-saving and low-carbon development model is one of the important symbols of high-quality economic development. This article attempts to study the environmental effects of green finance from both theoretical and empirical perspectives, that is, to test whether green finance policies contribute to achieving energy conservation and emission reduction. This article is based on provincial panel data from 2007 to 2020 in China and constructs a dynamic spatial Durbin model to examine the impact of green finance on environmental pollution and energy intensity. The results indicate that (1) green finance can achieve a dual effect of energy conservation and emission reduction simultaneously and has a significant promoting effect on energy conservation and emission reduction in neighboring regions. This conclusion is still valid after conducting robustness tests. (2) The energy-saving and emission-reduction effects of green finance exhibit significant regional heterogeneity, indicating that the performance of green finance is more outstanding in the eastern region with a higher level of economic development. (3) Mechanism testing has found that green finance can achieve energy-saving and emission-reduction effects through four channels: environmental regulation, credit allocation, enterprise profits, and enterprise innovation. Therefore, in order to further promote high-quality economic development, we need to build a comprehensive and multi-level green finance system, enrich the green finance policy toolbox, and smooth the transmission channels of green finance to promote green and stable economic development.

Nonlinear Spatial Dynamic Panel Data Models with Endogenous Dominant Units: An Application to Share Data

This article develops a nonlinear spatial dynamic panel data model with one particularly interesting application to a structural interaction model for share data. To account for effects from dominant (popular) units, the spatial weights matrix in our model can allow for unbounded column sums. To account for heterogeneity, our model includes two-way fixed effects and heteroscedastic errors. We further consider the potential endogeneity of the spatial weight matrix constructed from socioeconomic distance. We investigate the quasi-maximum likelihood estimator (QMLE), generalized methods of moments estimator (GMME), and root estimator (RTE), and establish their consistency and asymptotic normality based on the near epoch dependence (NED) framework. The RTE can derive a relatively computationally simple and closed-form solution without evaluating the QMLE’s Jacobian matrix as well as the iterations by GMME. We consider both n , T → ∞ , and the strength of the dominant units is equal to 1 when T → ∞ . For the purpose of empirical analysis, we derive the marginal effects and their limiting distributions based on the proposed estimators. In an empirical application, we apply our model to China’s prefecture city-level data, revealing significant spillover effects of the tertiary industry share. These findings suggest that the development of the tertiary sector in large cities can foster its growth in small cities.

The spatiotemporal effects on ecosystem services supply-demand from industrial transformation and upgrading: Promotion or inhibition?

Traditional industrial development models have already caused an imbalance in ecosystem services supply and demand (ESSD). As a response strategy, industrial transformation and upgrading (ITU) are increasingly playing a vital role in regulating the relationship among ESSD. However, research on the interplay and impact of these two aspects remains insufficient. Therefore, a comprehensive analysis of the complex effects of ITU on ESSD is essential for industrial planning and environmental management. We developed a theoretical framework to interpret how ITU impacts ESSD, from the perspective of the natural-social-economic composite ecosystem. Using the Yangtze River Economic Belt (YREB) as an example, we employed the Dynamic Spatial Durbin model (DSDM) to reveal the spatiotemporal effects of ITU on ESSD for four distinct types. The results indicated that from 2010 to 2021, the total supply of ecosystem services in the YREB exceeded the demand. The kernel density curves of ESSD ratios related to food production, carbon sequestration, water conservation, and soil conservation all show a trend of peak shifting to the bottom right over time. Additionally, pronounced spatial heterogeneity was observed in the ESSD ratios, with a notable clustering trend over time. Furthermore, the analysis of spatiotemporal effects decomposition revealed that ITU had a detrimental influence on the equilibrium between the supply and demand of food production, with this adverse effect intensifying and manifesting a stronger spatial spillover as time progressed. Positive effects were observed in the supply and demand for carbon sequestration and water conservation, both demonstrating significant spatial spillover effects. Steady negative spatiotemporal effects were noted for soil conservation supply and demand. Finally, we clarified the principal pathways through which ITU impacts ESSD. It offers policy recommendations for industrial development and ecosystem stewardship to mitigate the ESSD imbalance.

Land Use Modeling Scenarios using Spatial Dynamic Model in Badung Regency

Management of land use is necessary to maintain the balance between human needs and environmental aspects. Known as the highest-income region in Indonesia, Badung Regency has substantial demand for land utilization for tourism activities. The conversion of productive agricultural land to tourism areas would threaten the sustainability of natural ecosystems in Badung. This study aims to predict the future land use in Badung Regency in 2033 through two development scenarios, Bussiness As Usual (BAU) and Rapid Economic Growth (REG). The simulation was carried out using spatial dynamic models of cellular automata. Several satellite imageries, municipal data, field surveys, and influencing factors of land transition have been utilized as input for the model. The result indicates that the residential area dominated the development in the BAU scenario with the direction of residential development spreading to the south, and new tourism development portrayed in the north close to the agricultural area. While in the REG scenario, tourism land use had significant growth that does not only approach agricultural land but also protected forest areas in the north. The model accuracy showed moderate agreement according to the Kappa index both in BAU and REG scenarios. The finding of this study could support the decision maker to create a better policy considering the potential impact of land use growth direction.

Spatiotemporal Heterogeneities in the Impact of Chinese Digital Economy Development on Carbon Emissions

The digital economy plays an important role in reducing the global warming process. This paper explores the spatiotemporal heterogeneity impacts of the digital economy on provincial carbon emissions and its underlying mechanisms. Initially, this paper examines the inhibitory impact of the digital economy on carbon emissions, alongside the mediating role of economic agglomeration, at a theoretical level. Subsequently, it empirically explores the quantitative associations among the digital economy, economic agglomeration, and carbon emissions by utilizing Chinese provincial panel data spanning from 2000 to 2021. This investigation employed static and dynamic spatial Durbin models as well as mediation models to analyze the interrelationships. The results firstly revealed that the digital economy notably diminishes carbon emissions, with economic agglomeration playing a significant mediating role. This conclusion remained consistent even after substituting the explanatory variables and weight matrix, modifying the sample period, and conducting other robustness tests. Secondly, the impact of the digital economy on carbon emissions exhibited spatial spillovers. Compared with the impact on the local area, the impact on neighboring provinces was found to be weaker. Thirdly, carbon emissions showed a significant “snowball” effect in the time dimension. This paper emphasizes the important role of digital technology in curbing carbon emissions, and it provides some policy insights for studying the digital economy, economic agglomeration, and carbon emissions. Furthermore, it offers valuable insight and suggestions to reduce carbon emissions and realize the goal of the “dual-carbon” strategy.

Population mobility : spatial spillover effect of government health expenditure in China

ABSTRACT Background Since the 20th century, pursuing Universal Health Coverage (UHC) has emerged as an important developmental objective in numerous countries and across the global health community. With the intricate ramifications of population mobility (PM), the government faces a mounting imperative to judiciously deploy health expenditure to realise UHC effectively. Objective This study aimed to construct a comprehensive UHC index for China, assess the spatial effects of Government Health Expenditure (GHE) on UHC, and explore the moderating effects of PM on this association. Method A Dynamic Spatial Durbin Model (DSDM) was employed to investigate the influence of the GHE on UHC. Therefore, we tested the moderating effect of PM. Results In the short-term, the GHE negatively impacted local UHC. However, it enhanced the UHC in neighbouring regions. Over the long term, GHE improved local UHC but decreased UHC in neighbouring regions. In the short-term, when the PM exceeded 1.42, the GHE increased the local UHC. Over the long term, when the PM exceeded 1.107, the GHE impeded local UHC. If the PM exceeded 0.91 in the long term, the GHE promoted UHC in neighbouring regions. The results of this study offer a partial explanation of GHE decisions and behaviours. Conclusions To enhance UHC, a viable strategy involves augmenting vertical transfer payments from the central government to local governments. Local governments should institute healthcare systems tailored to the urban scale and developmental stages, with due consideration for PM. Optimising the information disclosure mechanism is also a worthwhile endeavour.

Spatial characteristics and dynamic modeling of informal economies

Spatial characteristics and dynamic modeling of informal economies

Dynamic modeling and vibration control optimization of a rotating hollow beam with ESACLD treatment

As an active and passive hybrid control technology, enhanced segmented active constrained layer damping (ESACLD) is an intelligent damping structure for suppressing adverse vibrations in flexible structures. In this study, the three-dimensional spatial dynamic model of a rotating hollow beam with improved ESACLD treatment is established based on the finite element method in a floating frame of reference. The damping effects of the ESACLD with both edge elements and incisions are considered in the dynamic model for the first time. Simulation results show the damping performance of the ESACLD is better than traditional active constrained layer damping with or without incisions (SACLD or ACLD). The Genetic Algorithm-Backpropagation (GA-BP) neural network is used to optimize the physical parameters of ESACLD, and the optimized model can effectively suppress the transverse and flapping bending vibration. It is also found that flapping and transverse bending vibrations exhibit differential dependence on nutation and precession angular velocities; higher nutation and lower precession velocities suppress flapping bending. The existing commercial finite element software has some problems, such as low efficiency and inaccuracy. The new finite element proposed in this paper uses a new form function to ensure accuracy in the simulation of rotation and vibration and has obvious advantages in dealing with rotational structures and complex boundary conditions. Research in this work can provide a design framework for vibration prediction and control of flexible beam-like structures.

Testing Spatial Dynamic Panel Data Models with Heterogeneous Spatial and Regression Coefficients

Spatio‐temporal data are often analysed by means of spatial dynamic panel data (SDPD) models. In the last decade, several versions of these models have been proposed, generally based on specific assumptions and estimator properties. We focus on an SDPD model with heterogeneous coefficients both in the spatial and exogeneous regression components. We propose a strategy to identify the specific structure of the SDPD model through a multiple testing procedure that allows to choose between a general version of the model and a nested version derived from the general one by imposing restrictions on the parameters. Our proposal can be used to test the homogeneity of the model parameters as well as the absence of specific components, such as spatial effects, dynamic effects or exogenous regressors. It is also possible to use the proposed testing procedure for the identification of relevant locations. The theoretical results highlight the consistency of the testing procedure in the high‐dimensional setup, when the number of spatial units goes to infinity and exceeds the number of time‐observations per spatial unit. Further, we also conduct a Monte Carlo simulation study, which gives empirical evidence of the good performance of the testing procedure in finite samples.

Temperature-dependent dispersal and ectotherm species' distributions in a warming world.

Dispersal is a crucial component of species' responses to climate warming. Warming-induced changes in species' distributions are the outcome of how temperature affects dispersal at the individual level. Yet, there is little or no theory that considers the temperature dependence of dispersal when investigating the impacts of warming on species' distributions. Here I take a first step towards filling this key gap in our knowledge. I focus on ectotherms, species whose body temperature depends on the environmental temperature, not least because they constitute the majority of biodiversity on the planet. I develop a mathematical model of spatial population dynamics that explicitly incorporates mechanistic descriptions of ectotherm life history trait responses to temperature. A novel feature of this framework is the explicit temperature dependence of all phases of dispersal: emigration, transfer and settlement. I report three key findings. First, dispersal, regardless of whether it is random or temperature-dependent, allows both tropical and temperate ectotherms to track warming-induced changes in their thermal environments and to expand their distributions beyond the lower and upper thermal limits of their respective climate envelopes. In the absence of dispersal mortality, warming does not alter these new distributional limits. Second, an analysis based solely on trait response data predicts that tropical ectotherms should be able to expand their distributions polewards to a greater degree than temperate ectotherms. Analysis of the dynamical model confirms this prediction. Tropical ectotherms have an advantage when moving to cooler climates because they experience lower within-patch and dispersal mortality, and their higher thermal optima and maximal birth rates allow them to take advantage of the warmer parts of the year. Previous theory has shown that tropical ectotherms are more successful in invading and adapting the temperate climates than vice versa. This study provides the key missing piece, by showing how temperature-dependent dispersal could facilitate both invasion and adaptation. Third, dispersal mortality does not affect the poleward expansion of ectotherm distributions. But, it prevents both tropical and temperate ectotherms from maintaining sink populations in localities that are too warm to be viable in the absence of dispersal. Dispersal mortality also affects species' abundance patterns, causing a larger decline in abundance throughout the range when species disperse randomly rather than in response to thermal habitat suitability. In this way, dispersal mortality can facilitate the evolution of dispersal modes that maximize fitness in warmer thermal environments.

Grain industrial agglomeration and grain green total factor productivity in China: A dynamic spatial durbin econometric analysis

Appropriate industrial agglomeration has several benefits, including reducing environmental pollution, promoting innovation and enhancing grain green total factor productivity (GTFP). It is an effective strategy for promoting high-quality and sustainable development in the grain industry. In this study, the slacks-based measure of the global Malmquist–Luenberger (SBM-GML) model is used to estimate China's grain GTFP using the panel data of 31 provinces from 2001 to 2020. Furthermore, a dynamic spatial econometric model is employed to empirically investigate the impact of grain industry agglomeration on grain GTFP and its regional heterogeneity. The results show that GTFP exhibits a fluctuating growth tendency, with advancements in green technology serving as the primary engine of that expansion. (2) The deepening of grain industrial agglomeration has a long-term promoting effect on the grain GTFP of local and neighbouring areas, with the long-term effect being more significant than the short-term effect, as revealed by the dynamic spatial Durbin model. (3) According to the heterogeneity analysis, industrial agglomeration's impact on grain GTFP is most noticeable in the production and sales balance. Therefore, encouraging regional cooperation and communication while raising the grain industrial agglomeration standard is crucial. The degree of regional economic growth, the state of the agricultural infrastructure and the conditions of the natural resources should all be considered by policymakers when developing distinct and focused policy assistance for each region.

Technological Innovation, Environmental Regulation, and Green Total Factor Productivity in the Logistics Industry: Dynamic Spatial Durbin Model Analysis

Technological Innovation, Environmental Regulation, and Green Total Factor Productivity in the Logistics Industry: Dynamic Spatial Durbin Model Analysis

The digital economy, market integration and environmental gains

Environmental governance has always been a focus of academic discourse. Innovatively, this study explores the environmental gains of the digital economy through the lens of market integration. Here, we provide several novel findings using staggered difference-in-differences and dynamic spatial Durbin models based on urban panel data from China from 2011 to 2019. First, the digital economy significantly improves air pollution, resulting in environmental gains for neighboring cities. Second, a heterogeneity analysis shows that the digital economy's environmental gains are more pronounced in regions with high endowment structures and urbanization rates. Finally, our mechanism analysis indicates that as commodity market segmentation and capital factor market distortion increase, the digital economy becomes more capable of improving air quality. The alleviation of capital factor market distortion and commodity market segmentation emerges as the mechanism through which the digital economy exerts its environmental gains.

The Regional Heterogeneity of the Impact of Agricultural Market Integration on Regional Economic Development: An Analysis of Pre-COVID-19 Data in China

The abrupt onset of the COVID-19 pandemic in late 2019 significantly disrupted China’s domestic agricultural production and supply chain stability. Local governments, responding to urgent circumstances, implemented various trade restrictions that profoundly affected regional economic development. This study, covering data from 2010 to 2019 across 31 provinces, investigates agricultural market integration and regional economic development. Employing a dynamic spatial panel Durbin model, it systematically analyzes the complex relationship between these variables. International trade variables related to agricultural products are then introduced to examine their “substitution effect” in promoting regional economic development through agricultural market integration. The research findings are summarized as follows: (1) disregarding international agricultural trade, a one-unit increase in the agricultural market integration index corresponds to a 0.156% rise in regional economic development. (2) In an open economy, the substitution coefficients for agricultural imports, exports, and total trade concerning market integration are −0.00097, −0.0012, and −0.0038, respectively. (3) The strength of the substitution effect from the international agricultural market to the domestic market varies regionally, with coefficients of −0.00099 and −0.00217 for the eastern and western regions, respectively.

Dynamic linkages between tourism development, renewable energy and high-quality economic development: Evidence from spatial Durbin model.

There has been a shift in focus toward environmentally and economically sustainable forms of economic growth known as High-quality economic development (HQED). However, this study analyzes the impact of tourism development (TD) and renewable energy consumption on HQED in 30 provinces of China, while covering the time period from 2007 to 2021. TD and HQED has been measured with help of Global Moran Index. This study has used dynamic spatial Durbin model (SDM) to measure the dynamic impact of TD index and renewable energy consumption on HQED along with green finance, foreign direct investment and investment in education. The findings from empirical analysis shows that TD has negative impact on HQED and in more developed regions, the relationship is positive, while in the less developed western part of China, the U-shape has been reversed. Central and northeastern China have a U-shaped connection, while it has been noticed the interaction term of TD and renewable energy endorses HQED. In addition, renewable energy consumption, green finance and increase in education investment have positive and significant impact on HQED while foreign direct investment has negative impact on HQED in China. Therefore, in the light of this study policymakers should focus on the quality of tourism industry, green finance for renewable energy supply and enhancing education investment in China to attain the goal of HQED.

Spillover effects of fiscal decentralization on access to basic social services in Burkina Faso

AbstractThis paper delves into the spillover effects of fiscal decentralization on access to essential social services within communes in Burkina Faso. It employs a Dynamic Spatial Durbin Model that comprehensively incorporates temporal dynamics, spatial interdependence concerning the dependent variable, and spatial interdependence concerning exogenous variables. The analysis covers 280 municipalities, which represents 80% of the municipalities in Burkina Faso, over the period 2011–2020. The findings substantiate the presence of spillover effects stemming from fiscal decentralization on the availability of fundamental social services. However, the nature of these spatial effects hinges on the particular type of fiscal resources and local public goods under consideration. Notably, both capital expenditures and decentralized cooperation resources yield short‐term and long‐term direct as well as indirect enhancements to the quality of education and accessibility to potable water. On the other hand, self‐generated revenue and operational grants augment water accessibility but bear no impact on educational quality. The outcomes put forth imply that municipal councils should concurrently bolster resource mobilization by fortifying decentralized cooperation and widening the tax base. Simultaneously, the introduction of flexible tax payment mechanisms is advisable.

Artificial intelligence, green technological progress, energy conservation, and carbon emission reduction in China: An examination based on dynamic spatial Durbin modeling

Artificial Intelligence (AI), as the core driving force of industrial and technological innovation, has brought technological advances that profoundly affect energy conservation and carbon emission reduction in China. This study constructs a dynamic spatial Durbin model to empirically analyze AI's mechanism path using Chinese inter-provincial panel data as a sample. The findings show the following: (1) Spatial and temporal variations exist in AI development levels and energy conservation and carbon emission reduction. (2) AI has a significant negative impact on energy consumption efficiency and a significant negative spatial spillover effect on carbon emission efficiency, and the effects of AI development level on energy conservation and carbon emission reduction are mainly dominated short term. (3) Green technological progress on both the input and output sides has not played a determinant role in the effects of AI on energy conservation and carbon emission reduction. (4) Given the moderating effect of the degree of factor market development, the improvement of green technological advances can play a positive role in the impact of AI on energy and carbon reduction. These findings suggest the need to provide policy support for energy conservation and carbon emission reduction by improving the spatial and regional linkage mechanism to narrow spatial and temporal differences in development levels, formulating AI policies addressing regional heterogeneity to promote the full transformation of AI's short-term effects into long-term effects as well as emphasizing the negative role of green technological advances and accelerating the transformation of green technological achievements, among other measures.