Spatial Model Research Articles

Carbon emission reduction enabled by informatization construction: an analysis of spatial effects based on China's experience.

The "dual-carbon" objective presents a huge challenge for China and the world, with profound implications for the advancement of China's eco-friendly economy. Additionally, informatization development has a significant impact on the level of carbon emissions in both local and neighbouring regions. Therefore, we employ panel data from 30 provinces in China spanning the years 2012 to 2021, and use the Kernel density estimate and Moran's index to explore informatization level and carbon emissions space agglomeration characteristics. We elucidate the nonlinear relationship and heterogeneity between informatization improvement and carbon emissions based on the spatial Durbin model. The primary findings are as follows. Firstly, we discover a distinct spatial clustering phenomenon which the informatization level is high in coastal areas and low in inland areas, whereas carbon emissions are low in the south and high in the north. Secondly, the effect of the informatization level on carbon emissions is shown as a U-shaped and non-linear correlation, signifying inhibitory and subsequently promoting phases. Thirdly, we reveal the negative influence on carbon emissions caused by spatial lag terms of the informatization level, and find that a higher local informatization level will have an inhibitory effect on carbon emissions in neighbouring areas. Finally, there is a spatial heterogeneity in the impact of the informatization level on carbon emissions, which presents the U-shaped relation between informatization level and carbon emissions varies across the North-South subregion and the three major economic subregion of China.

How planning intervention changes the “price tags” of property rights? Towards a dynamic understanding of China's small property right housing

While studies of property rights have long gone beyond the legal-illegal dichotomy, how to quantify the varying degree of property rights remains a challenging task. Focusing on a special type of informal housing—Small Property Right Housing (SPRH) in China, this study examines how planning intervention affects the pricing mechanism of SPRH through the theoretical lens of graded property rights. A previous study has unravelled the pricing mechanism of SPRH by quantifying the impact of informal institutions inherent to the SPRH market that give different “price tags” to varying degree of property rights, while not being able to capture the impact of external interventions. Drawing on two SPRH datasets before and after a Master Plan of urban villages conservation announced in 2019, we employ difference in difference (DID) estimations to examine how planning intervention affects SPRH prices by way of altering the strength of informal property rights. We also develop spatial error models (SEM) to account for spatial autocorrelation. This research extends and enriches the robust conceptualisation of graded degree of informal property rights by capturing the changes caused by external intervention to offer a dynamic and more accurate understanding of SPRH pricing mechanism. It also demonstrates that planning can go beyond sanctioning and redlining to tolerate and steer informal development.

Addressing the spatial disparity of COVID-19 vaccination services: A spatial optimisation approach

The COVID-19 pandemic has posed global public health threats. The mass COVID-19 vaccination has continuously played a central role in containing this pandemic. To maximise the benefits of COVID-19 vaccination, equal accessibility to vaccination at different spatial scales is critical. Limited research has investigated the spatial disparity and location optimisation of COVID-19 vaccination services. In this study, we utilised the spatial big data of COVID-19 vaccination sites and used the Gini index to measure the spatial disparity of COVID-19 vaccination service levels. Then, we leveraged spatial optimisation models to enhance the spatial deployment of vaccination sites in the study area of England, UK. The results demonstrate the feasibility and efficiency of the proposed methods. Specifically, the spatial disparity of vaccination services is largely mitigated by the optimised configuration of vaccination sites, as the Gini index decreases from 0.84 (before optimisation) to 0.12 (after optimisation). The suggested vaccination sites would improve the equality of vaccination services and reduce the financial costs of maintaining the vaccination programme, which has implications for public health policymakers. This research illustrates that spatial optimisation could be instrumental in effectively addressing public health crises.

Differential Analysis of Carbon Emissions between Growing and Shrinking Cities: A Case of Three Northeastern Provinces in China

Carbon emission issues are becoming increasingly severe, and the carbon emissions in shrinking cities, primarily characterized by population loss, are often overlooked and insufficiently studied. This paper focuses on the carbon emissions from county-level administrative units in China’s three northeastern provinces from 2001 to 2017. The study scientifically identified shrinking cities and measured the differences in carbon emission characteristics between growing and shrinking cities using the Theil index. Ultimately, the paper constructs a panel spatial econometric model to analyze the factors influencing them and explore their spatial effects. (1) The total carbon emissions in the Three Northeastern Provinces exhibited an inverted U-shaped trend, increasing from 734.21 million tons in 2001 to 1731.73 million tons in 2017, with the Mann–Kendall trend test showing a significant increase; spatially, this manifests as a significant positive spatial autocorrelation. (2) The region has 138 shrinking cities, accounting for over 50%; regarding carbon emission characteristics, the Theil index has consistently remained above 0.18, indicating significant differences between the carbon emissions of growing and shrinking cities. (3) The panel spatial econometric model results show that the influencing factors of carbon emissions in shrinking cities have unique directions, intensities, and spatial effects. In shrinking cities, aside from localized GDP effects and per-capita GDP acting as a suppressant, the population size has a pronounced inhibitory effect on local and surrounding carbon emissions. The analysis reveals significant differences in the carbon emission patterns and mechanisms between growing and shrinking cities; based on these results, the paper proposes differentiated carbon control strategies.

SpAbundance: An R package for single‐species and multi‐species spatially explicit abundance models

Abstract Numerous modelling techniques exist to estimate abundance of plant and animal populations. The most accurate methods account for multiple complexities found in ecological data, such as observational biases, spatial autocorrelation, and species correlations. There is, however, a lack of user‐friendly and computationally efficient software to implement the various models, particularly for large data sets. We developed the spAbundance R package for fitting spatially explicit Bayesian single‐species and multi‐species hierarchical distance sampling models, N‐mixture models, and generalized linear mixed models. The models within the package can account for spatial autocorrelation using Nearest Neighbour Gaussian Processes and accommodate species correlations in multi‐species models using a latent factor approach, which enables model fitting for data sets with large numbers of sites and/or species. We provide three vignettes and three case studies that highlight spAbundance functionality. We used spatially explicit multi‐species distance sampling models to estimate density of 16 bird species in Florida, USA, an N‐mixture model to estimate black‐throated blue warbler (Setophaga caerulescens) abundance in New Hampshire, USA, and a spatial linear mixed model to estimate forest above‐ground biomass across the continental USA. spAbundance provides a user‐friendly, formula‐based interface to fit a variety of univariate and multivariate spatially explicit abundance models. The package serves as a useful tool for ecologists and conservation practitioners to generate improved inference and predictions on the spatial drivers of abundance in populations and communities.

Secondary production of the central rangeland region of the United States.

Rangelands are the dominant land use across a broad swath of central North America where they span a wide gradient, from <350 to >900 mm, in mean annual precipitation. Substantial efforts have examined temporal and spatial variation in aboveground net primary production (ANPP) to precipitation (PPT) across this gradient. In contrast, net secondary productivity (NSP, e.g., primary consumer production) has not been evaluated analogously. However, livestock production, which is a form of NSP or primary consumer production supported by primary production, is the dominant non-cultivated land use and an integral economic driver in these regions. Here, we used long-term (mean length = 19 years) ANPP and NSP data from six research sites across the Central Great Plains with a history of a conservative stocking to determine resource (i.e., PPT)-productivity relationships, NSP sensitivities to dry-year precipitation, and regional trophic efficiencies (e.g., NSP:ANPP ratio). PPT-ANPP relationships were linear for both temporal (site-based) and spatial (among site) gradients. The spatial PPT-NSP model revealed that PPT mediated a saturating relationship for NSP as sites became more mesic, a finding that contrasts with many plant-based PPT-ANPP relationships. A saturating response to high growing-season precipitation suggests biogeochemical rather than vegetation growth constraints may govern NSP (i.e., large herbivore production). Differential sensitivity in NSP to dry years demonstrated that the primary consumer production response heightened as sites became more xeric. Although sensitivity generally decreased with increasing precipitation as predicted from known PPT-ANPP relationships, evidence suggests that the dominant species' identity and traits influenced secondary production efficiency. Non-native northern mixed-grass prairie was outperformed by native Central Great Plains rangeland in sensitivity to dry years and efficiency in converting ANPP to NSP. A more comprehensive understanding of the mechanisms leading to differences in producer and consumer responses will require multisite experiments to assess biotic and abiotic determinants of multi-trophic level efficiency and sensitivity.

Competition for territories under the switching cost

ABSTRACT This study considers an effective policy to mitigate market powers held by retail liquefied petroleum gas distributors in Japan. A bilateral spatial competition model is constructed to analyse the effect of switching costs of consumers on retail competition in designated areas where stores provide free delivery service at uniform prices. Model analysis shows that, at equilibrium, stores have exclusive delivery areas which are their natural territories given endogenously. If stores commit to their territories, promoting competition with the adjacent market would be effective. Whereas, if stores do not commit, policies to lower the switching cost are effective.

NiReMS: A regional model at household level combining spatial econometrics with dynamic microsimulation

ABSTRACT The heterogeneous spatial and individual impacts of the Great Recession, Brexit and COVID-19 have generated an important challenge for macroeconomic and regional/spatial modellers to consider greater integration of their approaches. Focusing on agent heterogeneity at the ITL 1 level in the UK, we propose the National Institute Regional Modelling System (NiReMS) – a synthesis of dynamic microsimulation with a spatial regional macroeconometric model. The model gives regional macro projections while allowing for household level inference. To showcase the model, we explore the impact of discontinuing the uplift in Universal Credit (UC) before the end of the pandemic and show that it led to more households consuming less. Importantly, the proposed framework highlights the unequal distributional impact across regions of the UK.

Evaluation of bridge structure damage based on deep learning identification model

Safety monitoring is an important part of bridge engineering construction and operation. At present, there is room for promoting the health monitoring and evaluation of small and medium-sized concrete bridges. In view of this, the study first models the spatial model and physical parameters of the bridge, and then builds the data of vehicle load and vehicle type. To reduce the complexity of data mapping, wavelet packet decomposition is used to analyze the data structure. And the physical field effect analysis is abandoned to directly mine the data relationship at both ends by using deep neural network. The data decomposition results show that the method can discard the temperature-induced effect. And the local decomposition results of the data meet the input of the neural network. The data measured by the sensor is added to the depth learning model for fitting. The overall and local fitting rates are more than 92%. The loss function converges quickly, and there is no gradient explosion. The model predicts the bridge structural damage caused by vehicle stress of four load categories, and the results show that the average fitting rate is 89.72%. Therefore, the identification path of the proposed deep learning model has positive significance for the evaluation of bridge structural damage. The main contribution of the study is to propose a deep learning-based method for bridge structural damage assessment. By modeling the spatial model and physical parameters of the bridge and combining data from vehicle load and vehicle type, the data structure was analyzed using the wavelet packet decomposition method to eliminate temperature-induced effects and data from sensor measurements were added to the deep learning model for fitting. This finding has positive implications for bridge structural damage assessment and can provide effective pathways and methods to monitor and evaluate the health status of small and medium-sized concrete bridges. This has important practical application value for the construction and operation of bridge engineering.

Analysis of Spatial Spillover Effects of Digital Inclusive Finance on Urban-Rural Income Gap

This article mainly analyzes the changes in urban-rural disparities under digital inclusive finance, which can be divided into three levels of research objectives, gradually and layer by layer. Firstly, based on the theoretical foundation of accelerating the development of rural financial markets and reducing economic disparities between rural and urban areas through digital inclusive finance, a panel threshold model is constructed to quantitatively analyze the threshold effect of digital inclusive finance on the urban-rural income gap, and to study how to reduce the threshold effect and narrow the digital divide between rural and urban areas; Secondly, from the perspective of spatial spillover, this study examines the positive or negative spatial spillover effects of digital inclusive finance on regional urban-rural income disparities, in order to analyze the direction of the impact of digital inclusive finance on urban-rural income disparities. Using the Spatial Panel Lag Model (SAR) and Spatial Durbin Model (SDM) to empirically test their inherent correlation and impact; Thirdly, there are differences in policies and resource allocation among provinces and cities, as well as differences in urban-rural income disparities in different regions. Based on this, a quantitative analysis model is constructed to analyze the path and methods of digital inclusive finance promoting urban-rural income disparities.

Intelligent Geo-Tour Route Recommendation Algorithm Based on Feature Text Mining and Spatial Accessibility Model

In view of the problems in planning and recommending tour routes, this paper constructs a feature text mining (FTM) method and spatial accessibility model (SAM) as the key factors for scenic spot recommendation (SSR) and tour route recommendation (TRR). The scenic spot clustering algorithm (SSCA) based on FTM was constructed by tourists’ text evaluation data mining. Considering the spatial attributes of scenic spots, the scenic spot topology tree algorithm (SSTTA) based on dynamic buffer spatial accessibility (DBSA) was constructed. The optimal scenic spots were recommended based on interest matching and spatial accessibility optimization. As to the recommended scenic spots, this paper proposes an optimal tour route recommendation algorithm (TRRA) based on SSTTA, which aims to determine the optimal adjacent section path structure tree (ASPST) with the lowest cost under travel constraints and transportation modes. The experiment verifies that the proposed algorithm can recommend scenic spots that match tourists’ interests and have optimal spatial accessibility, and the optimal tour routes with the lowest costs under certain travel constraints. Compared with the searched sub-optimal tour routes, the optimal tour route recommended by the proposed algorithm produces the lowest travel costs, and all the scenic spots in the tour route meet the tourists’ interests. Compared with the commonly used BDMA and GDMA methods, the proposed algorithm can determine the optimal routes with lower travel costs.

Spatial adequacy of afforestation in Poland: do afforestation needs and environmental preferences matter?

The article concerns the afforestation of agricultural land, especially soils of low quality classes with little suitability for agriculture. The assumption was made that in order to effectively implement afforestation and optimize spatial structures, these lands should be analyzed from the point of view of environmental and spatial conditions (including the quality of the land and its suitability for field crops, cohesion of the forest ecosystem, the presence of naturally valuable areas), according to the potential of a given area. Land that is ineffective in cultivation and does not improve the cohesion of the ecosystem should be treated as preferred for afforestation. The assessment of the spatial adequacy (optimization) of afforestation in the context of afforestation needs and predispositions was carried out for Poland in terms of municipalities using the spatial autocorrelation model and methods. The research results indicate the need to strengthen the effectiveness of solutions that take into account the endogenous potential of municipalities.

Towards food security: Exploring the spatio-temporal dynamics of soybean in India

India is the world’s largest edible oil importer, and soybean oil accounts for a major portion of those imports, with implications for the Indian economy. Despite being the 4th largest globally in terms of harvested soybean area and 5th largest in terms of production, India is still heavily dependent on imports to meet the vegetable oil requirement for its population. It is therefore imperative to understand the dynamics and trends in India’s soybean production to help the country achieve self-sufficiency in edible oils. This study provides the first spatially explicit analysis of soybean in India, using long-term spatial and temporal statistics at national and subnational levels, using spatial and temporal statistical analysis models to examine the historical trends and its future prospects. Our analysis details the overall soybean expansion across the country and the increase in production but we also note that the annual growth rate has declined in each consecutive decade even though the area continues to expand. The average national yield has been stagnant at around 1 T/Ha but for some of the low-producing districts, a higher yield of more than 3 T/ha is reported. For most major producing districts, soybean yields are below 1.5 T/Ha. The state of Madhya Pradesh which was the major soybean producer is now matched by the state of Maharashtra in terms of production, however, Madhya Pradesh still has the largest area under soybean. We analyzed soybean hotspot expansion in India and found that the mean center of the soybean area and production has shifted approximately 93 km towards the south and 24 km to the west as the crop is rapidly being adopted in the southern and western parts of India expanding the hotspot in these parts. District-level analysis showed that the total number of districts constituting hotspots of soybean cultivation in India has increased from 29 to 42 in three decades. Furthermore, analysis of soybean oil and meal consumption with respect to the national population, import, export, domestic production, GDP per capita, and price of soybean oil and meal suggests that soybean oil and meal are highly correlated with GDP per capita and population, indicating that consumption of soybean oil and meal is likely to increase as GDP per capita increases, and future demand is expected to rise with the anticipated growth in the Indian population. Increased soybean production can play a significant role in increasing national food security for India and reducing dependence on foreign oil imports and also help the economy with soy meal exports. Understanding the spatiotemporal variability in area and yield will help target interventions to increase production. Given the overall low yields with high variability in production, particularly in recent years primarily due to successive extreme rains and droughts in major producing districts and the overall need to increase production to meet the country’s demand, there is a pressing need for government policies and research aimed at narrowing the yield gap and developing soybean varieties that are more productive and resilient to climate change.

Fintech and financial stability: Evidence from spatial analysis for 25 countries

Fintech has experienced rapid advances in recent years. This study examines the impact of Fintech on financial stability for a group of 25 countries during 2013–2020. We adopt the novel Fintech-enabled financing volume to directly measure Fintech development. We utilise both the aggregate and disaggregated level of Fintech financing; the latter includes crowdfunding, business lending and consumer lending, each has a different funding process and default rates. We account for spatial dependence in financial stability across countries by employing various spatial models. Our findings first reveal that there is positive spatial dependence of financial stability across countries. It implies that financial stability has a positive spillover to neighbouring countries and validates the necessity of spatial analysis. Second, based on the Spatial Durbin Model which best describes our data, Fintech financing makes a positive local and cross-border contribution towards financial stability, irrespective of alternative weight matrices and sample sizes. Such positive impact is more profound in countries with smaller sizes of Fintech financing volume, and the cross-border effect is stronger with closer geographic proximity. Finally, crowdfunding enhances financial stability, whilst consumer lending has a contrasting destabilising effect.

Realtime 3D reconstruction algorithm based on improved AKAZE and multi-layer feature detection network

Simultaneous localization and mapping (SLAM) technology aims to solve the problem of how to determine the trajectory of robots by observing the environment and construct sparse spatial models. And the 3D reconstruction technology can further build dense spatial models based on the pose information. This paper proposes a dense 3D reconstruction algorithm based on improved accelerated-KAZE features and a multi-layer feature detection network. A multi-layer feature detection neural network is constructed to replace the bag-of-words model and optimize the loopback results, which improves the effect of pose estimation. The optimized algorithm improves the mapping effect and construct clear and robust indoor complex models. The pose estimation results show that the optimized algorithm improves the accuracy by 30% compared with ORB-SLAM2. Compared with the mapping effect of Kintinuous, ElasticFusion, ORB-SLAM2 and BundleFusion, the optimized models own the advantages of clearer details, richer information and no split layer.

Spatiotemporal Pattern and Spatial Convergence of Land Use Carbon Emission Efficiency in the Pan-Pearl River Delta: Based on the Difference in Land Use Carbon Budget

Research on land use carbon emission efficiency (LUCEE) in the Pan-Pearl River Delta (PPRD) can aid in formulating regional differentiated carbon reduction strategies. In this work, the inversion of carbon emissions using night-time light (NTL) data and the modified Carnegie Ames Stanford Approach (CASA) model were used to measure the net carbon emissions from land use (NCELU). On this basis, the SBM-undesirable model was used to assess the LUCEE. Additionally, the exploratory spatial data analysis (ESDA), Dagum Gini coefficient, and spatial convergence model were further introduced to analyze the spatial correlation, regional differences, and convergence trend of the LUCEE. Findings indicate that: (1) The NCELU showed an increasing fluctuation. During the period of 2006–2020, the NCELU increased from −168.58 million tons to −724.65 million tons. (2) The LUCEE exhibited a three-phase fluctuating downward trend of “decrease–rise–decrease”. The LUCEE first decreased from 0.612 in 2006 to 0.544 in 2008, then gradually increased to 0.632 in 2016, and finally decreased to 0.488 in 2020. Spatially, the LUCEE manifested a distribution characteristic of “high in the north and south, low in the middle”, with distinct spatial clustering features. (3) The overall Gini coefficient in the study period increased from 0.1819 to 0.2461. The primary contributor to the overall difference over the entire sample period was hypervariable density. (4) The PPRD and its various subregions displayed significant features of absolute and conditional β convergence. The speed of regional convergence from fastest to slowest was central &gt; west &gt; east, with the absolute convergence speeds of 0.0505, 0.0360, and 0.0212, respectively. Finally, policy recommendations are proposed to achieve regional carbon neutrality for the PPRD.

Uncovering physical activity trade-offs in transportation policy: A spatial agent-based model of Bogotá, Colombia

BackgroundTransportation policies can impact health outcomes while simultaneously promoting social equity and environmental sustainability. We developed an agent-based model (ABM) to simulate the impacts of fare subsidies and congestion taxes on commuter decision-making and travel patterns. We report effects on mode share, travel time and transport-related physical activity (PA), including the variability of effects by socioeconomic strata (SES), and the trade-offs that may need to be considered in the implementation of these policies in a context with high levels of necessity-based physical activity.MethodsThe ABM design was informed by local stakeholder engagement. The demographic and spatial characteristics of the in-silico city, and its residents, were informed by local surveys and empirical studies. We used ridership and travel time data from the 2019 Bogotá Household Travel Survey to calibrate and validate the model by SES. We then explored the impacts of fare subsidy and congestion tax policy scenarios.ResultsOur model reproduced commuting patterns observed in Bogotá, including substantial necessity-based walking for transportation. At the city-level, congestion taxes fractionally reduced car use, including among mid-to-high SES groups but not among low SES commuters. Neither travel times nor physical activity levels were impacted at the city level or by SES. Comparatively, fare subsidies promoted city-level public transportation (PT) ridership, particularly under a ‘free-fare’ scenario, largely through reductions in walking trips. ‘Free fare’ policies also led to a large reduction in very long walking times and an overall reduction in the commuting-based attainment of physical activity guidelines. Differential effects were observed by SES, with free fares promoting PT ridership primarily among low-and-middle SES groups. These shifts to PT reduced median walking times among all SES groups, particularly low-SES groups. Moreover, the proportion of low-to-mid SES commuters meeting weekly physical activity recommendations decreased under the 'freefare' policy, with no change observed among high-SES groups.ConclusionsTransport policies can differentially impact SES-level disparities in necessity-based walking and travel times. Understanding these impacts is critical in shaping transportation policies that balance the dual aims of reducing SES-level disparities in travel time (and time poverty) and the promotion of choice-based physical activity.

Do Factor Misallocations Affect Food Security? Evidence from China

The reasonable allocation of grain production factors is of vital importance to food security and agricultural development. To assess the impact of agricultural factor misallocation on food security, this paper, based on the panel data from China spanning from 2005 to 2019, conducted a comprehensive evaluation of agricultural factor misallocation and food security coefficients across 31 provinces in China, using a spatial Durbin model to examine the effects of factor misallocations on food security. The findings are as follows: (1) Production factor misallocation has significant negative impacts on food security, among them, capital misallocation and labor misallocation inhibiting food security in the local and neighboring areas, and land misallocation has a significant negative impact on food security in local areas, while its spatial spillover effect is no longer significant. (2) Mechanism analysis shows that capital misallocation and labor misallocation hinder the development of transportation infrastructure and the transfer of rural labor, thereby reducing food security. Land misallocation has accelerated the construction of transportation infrastructure, promoted the migration of rural labor, and helped ensure food security. (3) The regional heterogeneity test reveals that capital misallocation and labor misallocation hinder food security in major grain-producing areas and both sides of the Hu Huanyong Line (Hu Line). Meanwhile, land misallocation hinders food security development in various grain-producing areas, as well as the southeast of the Hu Line. Based on the above conclusions, this paper proposes suggestions to improve the efficiency of land and labor resource allocation, accelerate the construction of transportation infrastructure, and encourage the transfer speed of surplus agricultural labor.

Spatiotemporal evolutions and reasons of land-use carbon emissions in counties, Shaanxi Province, China

Global warming caused by factors such as the expansion of construction land poses a major threat to the sustainable development of China. As an important component of China's low-carbon development strategy, there is a relative lack of analysis of the spatial and temporal evolution of land-use carbon emissions and the influencing factors at county-level. In this study, we employed Emission-Factor Approach to estimate carbon emissions from land use in 107 counties, Shaanxi province, China. Our findings revealed construction land were the primary contributors to carbon emissions, showing a substantial increase from 2000 to 2020. There was positive spatial autocorrelation in carbon emissions among counties, forming distinct aggregation patterns around the City of Xi'an and both the southern and northern regions of Shaanxi. By utilizing the Spatial Durbin Error Model (SDEM), demographic factors emerged as key drivers of carbon emissions, indicating the significance of addressing population concentration to curb emissions. Furthermore, promoting coordinated development and adjusting the economic structure in different counties can mitigate both population concentration and carbon emissions. Emphasizing industrial development and investments can also effectively suppress carbon emissions. Additionally, managing transportation-related emissions can be achieved by enhancing public transportation services and regulating private car usage.

The impact of agricultural credit on the cattle inventory and deforestation in Colombia: a spatial analysis

The relationship between agricultural credit and agricultural production as well as the impacts on sustainable development (i.e., poverty alleviation, reduction of inequalities, food and nutrition security, and stimulation of economic growth) have been widely documented. The objective of this study is to analyze the impact of credit on cattle production and deforestation in Colombia through spatial panel data models. For this purpose, a departmental data panel for the period 2011–2020 was built, based on available information from public entities. The results suggest that in Colombia, the relationship between access to credit and cattle production is significant and can be either negative or positive. In addition, there is evidence of spatial dependence, meaning that cattle production in one department is being affected by cattle production in a neighboring department or by all the departments that make up the national territory. Regarding deforestation, results show that, although the number of cattle present in a department does affect its annual deforestation rate due to a poor coverage of sustainably intensified cattle ranching systems, there is no relationship between deforestation and the access to credit nor any spatial correlations.