Spatial Impact Research Articles

Assessing the spatial occupation and ecological impact of human activities in Chengguan district, Lhasa city, Tibetan Plateau

In this study, the ecological impact of human activities and the space occupied by construction and arable land on the Tibetan Plateau were examined, focusing on changes in the net primary productivity (NPP) as a key indicator of ecological health. With the utilization of land use data and multiyear average NPP data from 2002 to 2020, we analyzed the effects of the conversion of zonal vegetation into construction and arable land on carbon sequestration and oxygen release in Chengguan District, Lhasa city. Our findings indicated a marked spatial difference in the NPP among different land types. Regarding the original zonal vegetation, the NPP ranged from 0.2 to 0.3 kg/m2. Construction land showed a decrease in the NPP, with values ranging from 0.16 to 0.26 kg/m2, suggesting a decrease in ecological productivity. Conversely, arable land exhibited an increase in the NPP, with average values exceeding 0.3 kg/m2. This increase suggested enhanced productivity, particularly in regions where the original zonal vegetation provided lower NPP values. However, this enhanced productivity may not necessarily indicate a positive ecological change. In fact, such increases could potentially disrupt the natural balance of ecosystems, leading to unforeseen ecological consequences. The original zonal vegetation, with NPP values ranging from 0.12 to 0.43 kg/m2, exhibited higher ecological stability and adaptability than the other land types. This wider NPP range emphasizes the inherent resilience of native vegetation, which could sustain diverse ecological functions under varying environmental conditions. These findings demonstrate the urgent need for sustainable land use management on the Tibetan Plateau. This study highlights the importance of considering the ecological impact of land use changes in regional development strategies, ensuring the preservation and enhancement in the unique and fragile plateau ecosystem.

Digital financial inclusion and upgrading of consumption structure: Evidence from rural China

Based on the perspective of spatial economy, this paper focuses on the primary effects and spatial characteristics of Digital Financial Inclusion (DFI) on the upgrading of rural consumption structure (URCS) in China, conducting a literature review and theoretical analysis. It then uses statistical data collected over the years and the Digital Financial Inclusion Index (DFII) of Peking University to prepare panel data for 31 provinces in China (aside from Hong Kong, Macao, and Taiwan) from 2011 to 2020 for empirical testing. The results are as follows: DFI can considerably boost URCS, and there is a strong spatial neighbor impact, that is, it is affected by random shocks in surrounding provinces via its spatial effect; DFI has nonlinear characteristics in the process of fostering URCS, with the threshold variables of income level and family sizes; the impact of DFI on URCS is spatially heterogeneous, and the promotion of the eastern region is better than other zones. These results can inform policymakers about rural development and provide valuable references to push forward rural vitalization.

Towards green governance and real economy development: how digital technology affects health human resource allocation?

ABSTRACT The adoption and integration of digital technology have profoundly reshaped societal and environmental governance within the healthcare sector. This study aims to explore the influence of digital technology development on the allocation of healthcare human resources, intending to contribute to the net-zero carbon process. Utilizing panel data from 30 provinces and cities across mainland China during the period 2011 to 2020, we computed three unique indices for health resource density. Employing the spatial Durbin model, our analysis unveiled a gradual augmentation in the overall level of healthcare human resource allocation in China due to the spatial impact of digital technology development. Robustness tests substantiated a noteworthy positive spatial spillover effect of digital technology, contributing to the augmentation of healthcare human resource allocation at the regional level. Heterogeneity results revealed a notably more pronounced promotional effect in the eastern region, contrasting with the central and western regions. From the perspective of sustainable development, the development of digital technology can contribute to the advancement of environmentally friendly healthcare by optimizing the allocation of healthcare human resources. In conclusion, this study offers valuable insights for China to formulate effective policies for healthcare human resource allocation, contributing to the attainment of sustainable development goals.

Exploring the Spatiotemporal Effects of the Built Environment on the Nonlinear Impacts of Metro Ridership: Evidence from Xi’an, China

Exploring the correlation of the built environment with metro ridership is vital for fostering sustainable urban growth. Although the research conducted in the past has explored how ridership is nonlinearly influenced by the built environment, less research has focused on the spatiotemporal ramifications of these nonlinear effects. In this study, density, diversity, distance, destination, and design parameters are utilized to depict the “5D” traits of the built environment, while Shapley Additive Explanations with eXtreme Gradient Boosting (XGBoost-SHAP) are adopted to uncover the spatial and temporal features concerning the nonlinear relationship of the built environment with ridership for metro stations located in Xi’an. We conducted a K-means clustering analysis to detect different site clusters by utilizing local SHAP coefficients. The results show that (1) built environment variables significantly influence metro ridership in a nonlinear manner at different periods and thresholds, with the POI facility density being the most critical variable and the other variables demonstrating time-driven effects; (2) the variables of population density and parking lot density exhibit spatial impact heterogeneity, while the number of parks and squares do not present a clear pattern; and (3) based on the clustering results, the metro stations are divided into four categories, and differentiated guidance strategies and planning objectives are proposed. Moreover, the current work offers a more developed insight into the spatiotemporal influence of built environments on metro travel in Xi’an, China, using nonlinear modeling, which has vital implications for coordinated urban–metro development.

Mitigating farmland use carbon emissions: The dynamic role of farmland use transition

China's carbon emissions contribute to approximately one-quarter of the global greenhouse gas emissions, with farmland use carbon emission (FUCE) emerging as a significant contributor. Recognizing the global importance of the farmland use system and the spatial intricacies of FUCE, it becomes imperative to examine the spatial impact of farmland use transition (FUT) on FUCE. This study reveals noteworthy spatial correlation disparities between FUCE and various FUT forms within the Huang-Huai-Hai Plain. Employing spatial econometric models, we explore the direct and spillover effects of dominant farmland use transition (DFUT) and recessive farmland use transition (RFUT) on FUCE. Among these transformations, quantitative farmland use transition (QFUT), production function farmland use transition (PFFUT), and living function farmland use transition (LFFUT) exhibit substantial positive direct effects and spatial spillover effects on FUCE. Conversely, spatial structure farmland use transition (SSFUT) and ecological function farmland use transition (EFFUT) demonstrate noteworthy reduction effects on FUCE. In both the short and long run, SSFUT and EFFUT play vital roles in suppressing FUCE, with the long-term effects proving more pronounced than their short-term counterparts. These findings underscore the importance of optimizing farmland use spatial structure and promoting ecologically sound land use practices to mitigate FUCE effectively.

Time series prediction for production quality in a machining system using spatial-temporal multi-task graph learning

The quality of multiple-machining-feature parts in batch computer numerical control machining systems is affected by complex spatial-temporal relationships, making accurate prediction difficult. Traditional quality prediction methods are difficult to cope with the challenges posed by the time impact effects generated by batch production sequences and the spatial impact effects generated by the adjacency of machining features of thin-walled parts. Hence, the time series prediction for production quality in a machining system using spatial-temporal multi-task graph learning is proposed to analyze the spatial-temporal impact relationship of machining quality and achieve accurate quality prediction. Specifically, a graph learning-based framework to capture the coupled spatial-temporal effects is designed to represent the spatial topological and temporal relationship of the machining features of batch parts. A novel global-local multi-task spatial-temporal graph learning framework is constructed to simultaneously predict the time series quality of each machining feature at different spatial positions. The global multi-task model aims to extract spatial-temporal coupling data features (i.e., input parameters for machining features at different positions of batch parts) for machining feature clusters based on gated networks and spatial-temporal graph convolution with multi-head attention. Then, the local multi-task model for each feature cluster (round holes, rectangular grooves, etc.) performs individual time series quality prediction regarding multiple quality indicators. Experimental results on a production dataset of a computer numerical control machining system for thin-walled parts show that the proposed method is superior to the conventional models such as a simple spatial-temporal graph convolution method (3.0%, 18.3% and 11.0% improvement in R2, MAE and RMSE) or the model without graph approach (6.5%, 29.8% and 19.8% improvement in R2, MAE and RMSE).

Multidimensional Evaluation of Traditional Villages in Jiangnan Region, China: Spatial Pattern, Accessibility and Driving Factors

Assessment of the spatial distribution and accessibility of traditional villages is closely related to their development. However, the impacts of spatial heterogeneity on the accessibility of traditional villages remain largely unknown. A total of 644 national-level traditional villages in the Jiangnan region were used to explore the spatial heterogeneity impact with a geographically weighted regression (GWR) model. We found: (1) spatially, the villages exhibit a predominant aggregation distribution pattern with significant local spatial disparities (R < 1, p < 0.01), predominantly originating from the Song and Ming dynasties (59.21%). Their clustering shifts from northeast to southwest, with over 70% of the villages located on slopes ranging from 0° to 20° and within 1 km of water. (2) The accessibility of these villages is generally low, with 85.66% being accessible within 200 ~ 300 min; it decreases concentrically outwards from Changzhou and exhibits clustering autocorrelation characteristics (Moran’s I > 0, Z > 2.58). (3) Road network density, elevation, and slope are significantly correlated with accessibility (p < 0.05), demonstrating pronounced spatial heterogeneity in their effects, with these factors collectively explaining approximately 85% of the accessibility levels. These findings provide a basis for comprehensive planning and categorized strategies for traditional villages.

THE SPATIAL SPILLOVER EFFECT OF INNOVATIVE CITY POLICY ON CARBON EFFICIENCY: EVIDENCE FROM CHINA

The strengthening of the deep integration of innovation and green is a necessary measure for sustainable urban development. Based on the improved Global-EBM, this study regards the innovative city policy as a quasi-natural experiment and evaluates its spatial spillover effects and potential impact channels. The results show that (1) The innovative city policy enhances the urban total factor carbon emission efficiency (TFCEE) and has positive spatial spillover effects on surrounding regions. (2) The mechanism test shows that the effective channels for innovative city policies to enhance TFCEE and generate spatial spillover effects come from improving the degree of green technology and resource allocation. (3) The spillover effect of innovation city policy is more significant for developed, coastal and advanced industrial structures. This study strengthens the theoretical foundation for promoting cross-regional green coordinated development and provides valuable suggestions for policymakers.

Analysis of the Spatial Spillover Effect and Impact Transmission Mechanism of China’s Water Network by Constructing a Water Transfer Information Weight Matrix

In China, the water network project plays a pivotal role in optimizing water resources allocation, enhancing regional water resources carrying capacity, and bolstering high-quality economic development. This study is grounded in the spatial interconnection of water resources, serving as the foundation for constructing a spatial measurement model. Leveraging data from 558 panel samples encompassing 31 provinces (including municipalities and districts) in China between 2003 and 2020, this research unveils the inherent correlation between the establishment of the water network and economic as well as social development. The findings indicate the following: (1) Considering inputs, outputs, and nonconsensual outputs, regional disparities in the SBM (slacks-based measure) value of the water network exist, demonstrating an overall increasing trend annually. In 2020, the nationwide average benefit level of input–output in water network construction reached 0.603. (2) Moran’s I test, predicated on the weight matrix of spatial water transfer information, reveals a spatial positive autocorrelation. All tests pass the significance threshold of 5%, affirming the presence of spatial agglomeration due to project construction, operation, and the interconnectedness of water resources. (3) SDM (spatial Durbin model) regression analysis elucidates that per capita GDP, resource endowment, technological innovation level, consumption index, and average wage significantly influence the growth of water network efficiency. Specifically, per capita GDP and the consumption index exert negative influences. Moreover, aspects such as regional resource endowment, technological innovation level, industrial and agricultural water demand, average wage, and other spatial dependencies exhibit a notable positive spatial spillover effect. (4) The SDM model suggests that per capita GDP growth fails to yield a significant spatial spillover effect on neighboring regions. Instead, it highlights a substantial indirect effect and spatial dependence of government attention among regions. These analyses are instrumental in optimizing the water resources allocation network system and enhancing investment efficacy.

Light and Emotional Architecture: An Embodied Experience

The presented explorative research focuses on the phenomenological relationship and emotional impact of light within architectural spaces, grounded in a pilot study and literature review. Serving as the foundational framework for a Ph.D., this study aspires to decipher the role of light in Emotional Architecture—a term introduced by the German artist Mathias Goeritz in his 1953 architectural manifesto crafted in Mexico. This multidimensional pilot study employs experiential observation and structured interviews, complemented with qualitative and quantitative documentation, all anchored by literature-based insights. Set in the Barragán House, which served as both the studio and residence for the renowned Mexican architect Luis Barragán since 1948, the study endeavours to elucidate how architectural nuances—like form, materials, colours, and arrangements—affect lighting conditions, subsequently shaping human emotions and experiences. Through twenty-two interviews, the research captured perceived atmospheres and the inherent intentionality of affordances in architecture, revealing a strong correlation between spatial impact on observers and light-related experiences. The interplay between these elements suggests a compelling avenue for implementing Emotional Architecture conducive to both individual and societal sustainable development.

Effectiveness of Financing Measures to Reduce Low Emissions in Poland - a Case Study

Local authorities are increasingly facing the challenges of climate change, which requires adaptation of various elements of local government systems, including infrastructure, communities and local economies. Operating in an ever-changing reality, local government units should plan for the necessary sustainable local government investments, with particular attention to structural gaps and financial mismatches. Local government is responsible for ensuring local energy security and thus for 'environmental sustainability'. The overall objective of this study is to assess the effectiveness of financing low-emission lowering measures from the Environmental Protection and Water Management Fund in municipalities in Lubusz Province in 2020. The article uses cause-effect relationship analysis (regression models). Investments implemented by local governments generally have a positive impact on the reduction of low emissions, although the results of the study indicate a negative impact of financing investments in alternative energy sources (RES). This may be related to large investment outlays, as well as difficulties in spatial planning, environmental impact and administrative inefficiency.

The spatial impact of digital economy on carbon emissions reduction: evidence from 215 cities in China

Introduction: Within the global framework of carbon emissions constraints, the digital economy has become a new strategy for cities to achieve sustainable development. Scholarly literature exploring the spatial spillover and spatial mechanisms of the digital economy on carbon emissions is notably scarce.Methods: To estimate the spatial impact of digital economy on carbon emissions, this paper conducted spatial analysis with the spatial Dubin model and panel data of 215 cities in China from 2011 to 2019.Results: The results show that there is a growing regional agglomeration of the digital economy, whereas the spatial evolution of carbon emissions displays low liquidity and high stability. Second, the digital economy directly reduces urban carbon emissions, and this conclusion is supported through a series of robustness tests. However, there exist negative spatial spillover effects of digital economy on carbon emissions reduction in neighboring cities. Third, mechanism analysis reveals that the digital economy mainly affects urban carbon emissions through two paths: industrial structure upgrading and green technology innovation. Moreover, the influence of digital economy exhibits heterogeneity, with a more pronounced effect observed in the central cities and in large and medium-sized cities, as well as in cities with a high agglomeration of the new energy industry.Discussion: Our paper not only presents new documentary evidence for understanding the relationship between digitalization and decarbonization, but also provides specific references for policy making to accelerate low-carbon urban development.

Geographical and temporal weighted regression: examining spatial variations of COVID-19 mortality pattern using mobility and multi-source data

The COVID-19 pandemic has had profound adverse effects on public health and society, with increased mobility contributing to the spread of the virus and vulnerable populations, such as those with pre-existing health conditions, at a higher risk of COVID-19 mortality. However, the specific spatial and temporal impacts of health conditions and mobility on COVID-19 mortality have yet to be fully understood. In this study, we utilized the Geographical and Temporal Weighted Regression (GTWR) model to assess the influence of mobility and health-related factors on COVID-19 mortality in the United States. The model examined several significant factors, including demographic and health-related factors, and was compared with the Multiscale Geographically Weighted Regression (MGWR) model to evaluate its performance. Our findings from the GTWR model reveal that human mobility and health conditions have a significant spatial impact on COVID-19 mortality. Additionally, our study identified different patterns in the association between COVID-19 and the explanatory variables, providing insights to policymakers for effective decision-making.

The role of informal ruralization within China’s rapid urbanization

In China’s rapidly urbanizing cities, chengshi kaihuang (CK), an informal practice of urban agriculture, has been quietly encroaching into neglected urban spaces for the cultivation of vegetables. China’s unprecedented transformation from an agrarian nation to a nation of megacities over the past four decades has relied massively upon the incorporation of rural land and people in the construction and operation of its cities. Yet, while some scholars have begun to unpack the complex agency of rural bodies in China’s urban environments, the rural is generally understood to be residual or obsolete against the overwhelming, top-down power of urbanization. Here we propose, through a remote sensing study of the practice, CK as an example of the bottom-up, ruralizing agency of ordinary people within China’s rapid urbanization and present data on the spatial impact of CK’s ruralization upon the central urban districts of Wuhan, a Chinese megacity.

The temporal and spatial evolution of green finance and carbon emissions in the Pearl River Delta region: An analysis of impact pathways

The issue of carbon emissions has become increasingly urgent in tandem with the rapid growth of the economy in the Pearl River Delta region. This paper uses kernel density estimation and standard deviation ellipses based on Pearl River Delta data from 2012 to 2021 to explore the spatiotemporal evolution trends of green financing and carbon emissions. Additionally, it uses a spatial Durbin model to examine the spatial effects of green finance on carbon emissions. The results indicate the following: Firstly, over the years, green finance shows an overall increasing trend, with an internal spatial pattern characterized by higher levels in the northeast and lower levels in the southwest. In contrast, carbon emissions are decreasing, with an internal spatial pattern characterized by higher levels in the southwest and lower levels in the northeast. Secondly, more and more green financing initiatives are aiming towards the Pearl River Delta's northeast, suggesting a more significant concentration of such initiatives there than elsewhere. At the same time, carbon emissions seem to converge on the southwest of the Pearl River Delta, which could indicate that this area generally has greater carbon emission levels. Finally, the spatial effect results reveal that improving green finance levels can significantly inhibit carbon emission levels in the Pearl River Delta region. Evidence of spatial spillover effects indicates that the carbon emissions in neighboring areas are influenced to some extent. This study on green finance and carbon emissions' spatiotemporal characteristics and spatial impact gives a development path and theoretical support for sustainable development goals in the global economy.

Evaluation of Land Use and Land Cover Change and Spatial Ecological Impacts Using GIS: A Case Study of Urumqi's Primary Urban Area

In this study, GIS technology was used to take the main urban area of Urumqi as an example to conduct an in-depth study of the land use cover change and its spatial ecological effects in the past 30 years. It is found that the rapid urbanization process has led to the gradual reduction of arable land and vacant land, while the area of land designated for construction has witnessed a notable upward trajectory. The alteration in land use has had a profound effect on the ecological environment, changed the original ecological pattern, and has had a non-negligible impact on air quality, soil fertility and biodiversity. Therefore, advocating reasonable land use and proposing and implementing sustainable urban development strategies are very important for the future development of the main urban area of Urumqi.

The Interests of Local Communities in the Transboundary Dimension: Considerations on the Dispute Regarding the Turów Lignite Mine

Abstract The objective of this article is to look for the answer to the question of whether the applicable public law regulations contain adequate mechanisms enabling the realisation of the interests of local communities in the transboundary dimension. The background to the considerations will be an analysis of the spatial planning and environmental impact assessment procedures in connection with the planned expansion of the area of the open cast Turów lignite mine.

Spatial Effects of Energy System Digitization on Carbon Emissions: Evidence from China

This study empirically examines the spatial effects and spatial mechanisms of energy system digitization on carbon emissions by using the projection pursuit method and spatial Durbin model with panel data of 30 provinces in China from 2013 to 2021 as samples. The results show that (1) the digitization of the energy system reduces the carbon emission intensity of the surrounding areas by 2.069%, which has a significant spatial emission reduction effect. (2) Technological innovation and industrial structure optimization are important spatial impact mechanisms. (3) The spatial emission reduction effect of energy system digitization is significant in the eastern region, but not in the central and western regions, indicating that the spatial emission reduction effect of energy system digitization is spatially heterogeneous.

The impact of digital infrastructure on industrial chain resilience: evidence from China's manufacturing

ABSTRACT The improvement of digital infrastructure is a key support for enhancing the industrial chain resilience and ensuring its normal operation under various unconventional disturbance risks. This paper uses panel regression model, quantile regression model, panel threshold model, and Spatial Durbin Model to explore the internal mechanism and spatial impact effect of digital infrastructure construction on China's manufacturing industry chain resilience. Research found that digital infrastructure does have a positive impact on industrial chain resilience. The estimation results of heterogeneity indicate that compared with underdevelopment or regions with tight finance, the positive promotion effect of digital infrastructure on the manufacturing industry chain in regions with developed economies and abundant regional finance is smaller. In regions with relatively abundant human capital, it is easier to promote the positive effect of digital infrastructure on industrial chain resilience. This paper not only enriches the quantitative research on industrial chain resilience, but also expands the research boundaries of industrial chain resilience. And it provides policy inspiration for promoting digital infrastructure and industrial chain resilience development in a coordinated manner.

Geodetic signature of a weak Lithosphere-Asthenosphere Boundary in postseismic deformation of large subduction earthquakes

The rheology of the seismically imaged Lithosphere-Asthenosphere Boundary (LAB) is an important new question in plate tectonics. If the LAB is associated with ponding of partial melts as inferred from many seismic studies, it should have a low viscosity that minimizes resistance to plate motion and subduction. Here, we propose that recently observed postseismic enhanced landward motion (ELM) in coastal areas outside the rupture zone of large subduction earthquakes provides the first geodetic evidence for a low-viscosity LAB beneath subducting oceanic lithosphere. Using three-dimensional viscoelastic finite element models, we demonstrate that a low-viscosity LAB readily leads to postseismic ELM over broad coastal areas far beyond the rupture zone. If the LAB thickness is assumed to be 10 km, key spatiotemporal characteristics of the observed ELM can be well explained by an LAB viscosity of 5 × 1016 Pa s, orders of magnitude lower than typical asthenospheric viscosities. A thicker LAB such as 20 km with a higher viscosity may also explain the postseismic ELM but is incompatible with existing, albeit limited, near-field vertical deformation observations. The small thickness and the low viscosity of the model LAB are consistent with inferences from seismological observations and support the notion of ponding melts. Our preliminary weak-LAB models also predict diagnostic deformation features that can be tested by future onshore and offshore geodetic observations. Whether a weak LAB is ubiquitously present before and after plate subduction and how its presence and spatial heterogeneity impact postseismic and plate-scale deformation are important research targets for future investigations.