Urban Development Strategies Research Articles

Enhancing urban public service efficiency through the national civilized city policy: an empirical analysis of 282 cities in China from 2005 to 2019

ABSTRACT Public service supply is a cornerstone of urban functionality, with enhancing efficiency being pivotal for sustainable urban development. In China’s urban development strategy, the National Civilized City (NCC) policy stands out as a key tool aimed at bolstering public service efficiency. By analyzing data from 282 Chinese cities spanning 2005 to 2019 and employing the ‘authoritative incentive-technology control’ framework, this study utilizes a fixed-effect regression model to delve into the mechanisms underlying the NCC policy’s impact on public service efficiency. Key findings include: (1) Notably, the NCC policy significantly improves urban public service efficiency, primarily propelled by directives from central authorities (2) The effectiveness of the policy varies across administrative tiers, with a more pronounced impact observed in lower-level cities.(3) Technical control mechanisms, particularly constraint controls, predominantly sustain policy effects (4) The enhancement of urban public services in China hinges on top-down policy incentives, emblematic of a dynamic central-local relationship characterized by the upward transfer of policy power and governance decentralization.

Observing the dynamics of urban growth of Al-Baha City using GIS (2006–2021)

This study observes and records land cover changes in Al-Baha to track urban growth. The study develops a schematic vision of urban mass's current and future shape and distribution and its growth to achieve sustainable urban development, reduce service and social differences between city areas, and integrate urban and rural areas. The study uses inductive methods to monitor urban developments, population growth, and land use through Al-Baha's structural schemes by extracting digital data from satellite imagery to survey historical land cover changes. The mapping software ArcGIS v10.8 processed these digital data and the final map compositions. Through this urban mass study, the research predicted city growth and development. Capabilities and functions must shape urban and rural-urban communities. The development must also consider the National Urban Development Strategy, the relative importance of rural-urban communities, and their expected functional role in influencing the development of small rural communities adjacent to them to reduce migration from rural areas near the city. The study highlighted changes in city land coverage. Al-Baha expanded from 1982 to 1995. Al-Baha's transformation from village to city accelerated, increasing its area from 31 km2 to 44 km2. Many villages beyond 40 were included in the decisions. From 2006–2021, the city's area grew 221%. It increased from 60 km2 to 133 km2 in an urban area, indicating false urban growth. The study stressed the importance of geographic information systems (GIS) and remote sensing (RS). The study recommends urban development in suitable areas because agricultural development is not feasible, especially in the northeast. Land use changes should be prevented by planning regulations because these agricultural areas are essential. Helping urban development move away from agricultural areas, especially on the eastern axis, is crucial. Create new urban centers along Al-Baha's western cliff to attract residents and relieve pressure. Limited linear expansion, especially in the northeast, and more expansive urbanization are also significant. Maps should also show when the Al-Baha region's expansionary scope changed and the urban boundaries that had to be reached by a specific time while development plans were efficiently monitored.

Exploring urban housing disadvantages and economic struggles in Seoul, South Korea

The present study investigates urban poverty in Seoul, South Korea, amid the COVID-19 pandemic, focusing on housing and economic challenges. Employing principal component analysis, clustering algorithms, and visualization techniques, it analyzes archived data to uncover disparities in housing conditions and economic well-being across Seoul. The research reveals significant socio-economic divisions, with over 75% of the city’s areas marked by vulnerability, indicating widespread poverty or the concentration of economically disadvantaged populations. This highlights the pervasive nature of poverty and the precarious situation of the urban poor, who are at risk due to fragile living conditions. The findings advocate for inclusive urban development strategies that prioritize the needs of marginalized groups, suggesting a shift from focusing solely on economic growth to ensuring equitable welfare for all residents.

Assembling plateau urbanism through special economic zones evidence from the Tibetan Plateau, China

This paper presents an in-depth analysis of urbanization dynamics on the Tibetan Plateau through the lens of Special Economic Zones (SEZs), addressing a research gap in the understanding of urbanism in highland and global south contexts. Given the current surge of assembly-based thinking within urban scholarship, we employ an assemblage framework to comprehend the heterogeneous constitution of urbanization, integrating human and non-human components, as well as socio-material, political, and spatial governances. Through a meticulous examination of three comprehensive case studies from the Tibetan Plateau SEZs, including a state-led new town, a renewable energy-driven industrial zone, and an industrial enclave, we delineate the diverse forms of plateau urbanism. Our findings underscore the imperative for contextually tailored urban development strategies that consider the distinctive geographical, ecological, and socio-political conditions of the Tibetan Plateau. It emphasizes the need for adaptive governance and planning that can address infrastructure deficiencies, environmental constraints, and the integration of local communities. Furthermore, we also call for further exploration of urbanisms in other highland regions, advocating for a more longitudinal, crosswise and nuanced understanding of urbanization processes in various global contexts.

Under the banner of heritage conservation: The gentrification of dilapidated low-income neighborhoods in the Daming Palace Protection and Renovation Project

Understanding the dynamics and implications of urban redevelopment projects, particularly in the context of China's rapid urbanization, could inform sustainable and inclusive urban development strategies. This abstract provides an overview of the Daming Palace Protection and Renovation Project (DPPRP). Unlike other redevelopment projects that prioritize economic benefits, the DPPRP is predominantly driven by political interests, with cultural and economic factors playing supporting roles. As part of the broader integrated conservation and redevelopment program, the DPPRP holds political significance, and its implementation demonstrates clear state involvement, led by the local government. The response from residents reflects the failure of China's attempts to promote citizen participation in local governance, suggesting challenges even for participatory authoritarianism. These developments have negative implications for Chinese citizens' pursuit of their right to the city, hindering their ability to shape urban spaces and participate meaningfully in decision-making processes. The purpose of this study is to analyze the Daming Palace Protection and Renovation Project (DPPRP) in China, providing insights for policymakers and urban planners to enhance the effectiveness and inclusivity of urban redevelopment projects.

Analysis of the spatiotemporal velocity of annual precipitation based on random field

. Changes in precipitation directly impact river runoff volume, subsequently influencing food production, and the security of downstream urban areas. In this study, we introduce a random velocity field (RVF) capable of performing multi-step predictions while providing interpretable insights into precipitation variations. The RVF leverages the gradient of a Gaussian random field to learn spatiotemporal velocity patterns and employs a predictive process to reduce dimensionality and enable multi-step forecasting. Bayesian parameter estimation is obtained using the Markov Chain Monte Carlo (MCMC) method. Our analysis reveals a noticeable shifting trend in annual precipitation based on diverse real datasets. This trend serves as a valuable foundation for further exploration of urban flood control and agricultural development strategies.

Vertical Extension and Retrofitting of the Existing R.C.C. Commercial Building – A review

This study explores the feasibility of vertically extension of existing buildings as a sustainable solution to urban densification, particularly focusing on the structural implications and potential benefits of such extensions. Through structural analysis using advanced software like ETAB, and SAFE. as well as examination of cost-effectiveness, the research evaluates the viability of vertical extensions in different contexts. Case studies from various regions, illustrate the technical challenges and benefits associated with vertical extension projects. The analysis encompasses considerations such as structural stability of existing building after floor extensions, and cost implications, offering insights into the most efficient methods for extending buildings vertically. This research underscores the importance of vertical extension as a sustainable urban development strategy and calls for greater awareness and adoption of this approach within the construction industry. It emphasizes the need for collaborative efforts among stakeholders and proposes a development process to streamline the implementation of vertical extension projects.

TRANSPORT AND LOGISTIC SYSTEMS IN THE SMART CITY CONCEPT

Topicality. The impact of a number of factors on climate change can be defined as twofold. Firstly, by reducing the number of pollutants responsible for global warming, and secondly, by increasing the resilience of systems (economic, environmental, social) to the vulnerability of these factors. A huge number of scientific and professional developments, strategic documents, programmes and implementation plans are devoted to these issues. Nevertheless, a tangible effect of mitigating the impact of negative environmental factors, of which transport and logistics systems (TLS) play a leading role, including TLS of urban areas, can only be achieved through an integrated approach, combining the cooperation of various stakeholders with different public interests and goals.Aim and tasks. The purpose of the study is to establish priorities and directions for the formation of new type of transport and logistics systems in the concept of a smart cityMaterials and Methods. To carry out the research, the method of processing documented data from primary sources was used – scientific publications, expert opinions, documents of municipal authorities on this issue.Research results. The peculiarity of the concept of sustainable development lies in the contradiction of its defining postulates to traditional economic mechanisms of unlimited economic growth, maximum state regulation, and profit-making by any means, without coordinating their strategies with the needs of the environment that these urban agglomerations use. It is worth noting that the vast majority of scholars and decision-makers on urban development strategies adhere to the concept of a "smart" city in terms of its intellectual provision with technologies, use algorithms, advanced digital infrastructure, etc. The economic sustainability of the transport system should aim to plan transport projects, resource consumption, etc. in a way that promotes economic growth in the long term, as the increase in the number of smart cities, as a resource-intensive and time-consuming process, is one of the mechanisms for accelerating the modernisation of the national economy as a whole. The concept of expanding the area of smart cities contributes to the expansion of domestic consumption in the region, modernisation of industry, and coordination of economic and social activities between regions.Conclusion. Taking as a basis the principles of sustainable development of transport and logistics systems of cities on the basis of the introduction of green logistics can reduce numerous environmental problems that have become a consequence of industrial modernization and globalization, and ensure the reduction of external environmental effects by balancing environmental, social and economic goals by transport and logistics enterprises.

The role of random forest and Markov chain models in understanding metropolitan urban growth trajectory

IntroductionThis study delves into the spatiotemporal dynamics of land use and land cover (LULC) in a Metropolitan area over three decades (1991–2021) and extends its scope to forecast future scenarios from 2031 to 2051. The intent is to aid sustainable land management and urban planning by enabling precise predictions of urban growth, leveraging the integration of remote sensing, GIS data, and observations from Landsat satellites 5, 7, and 8.MethodsThe research employed a machine learning-based approach, specifically utilizing the random forest (RF) algorithm, for LULC classification. Advanced modeling techniques, including CA–Markov chains and the Land Change Modeler (LCM), were harnessed to project future LULC alterations, which facilitated the development of transition probability matrices among different LULC classes.ResultsThe investigation uncovered significant shifts in LULC, influenced largely by socio-economic factors. Notably, vegetation cover decreased substantially from 49.21% to 25.81%, while forest cover saw an increase from 31.89% to 40.05%. Urban areas expanded significantly, from 7.55% to 25.59% of the total area, translating into an increase from 76.31 km2 in 1991 to 258.61 km2 in 2021. Forest area also expanded from 322.25 km2 to 409.21 km2. Projections indicate a further decline in vegetation cover and an increase in built-up areas to 371.44 km2 by 2051, with a decrease in forest cover compared to its 2021 levels. The predictive accuracy of the model was confirmed with an overall accuracy exceeding 90% and a kappa coefficient around 0.88.DiscussionThe findings underscore the model’s reliability and provide a significant theoretical framework that integrates socio-economic development with environmental conservation. The results emphasize the need for a balanced approach towards urban growth in the Islamabad metropolitan area, underlining the essential equilibrium between development and conservation for future urban planning and management. This study underscores the importance of using advanced predictive models in guiding sustainable urban development strategies.

SUSTAINABLE SMART CITIES: THE ROLE OF FINTECH IN PROMOTING ENVIRONMENTAL SUSTAINABILITY

Sustainable smart cities are at the forefront of urban development, striving to balance economic prosperity with environmental sustainability. This Review delves into the critical role played by Financial Technology (Fintech) in advancing environmental sustainability within smart city initiatives. Fintech innovations are instrumental in optimizing resource management, enhancing citizen engagement, and fostering collaborative governance models, thereby contributing to the overarching goal of creating sustainable urban ecosystems. Through a historical perspective, this Review traces the evolution of sustainable smart cities, highlighting the transformative impact of technological advancements and the integration of Fintech solutions. From early urban planning concepts to the emergence of smart technologies in the 21st century, the evolution of sustainable smart cities underscores the importance of leveraging innovation to address urban challenges. The rise of Fintech has further accelerated this evolution, offering innovative financial services tailored to support green finance, renewable energy investment, and sustainable infrastructure development. Fintech platforms enable efficient allocation of resources, facilitate transparent and secure transactions, and empower citizens to participate in sustainable initiatives. By integrating Fintech solutions into urban development strategies, cities can unlock new opportunities for environmental sustainability while fostering economic growth and social equity. Looking ahead, the integration of sustainable smart cities and Fintech holds immense potential to address pressing environmental challenges such as climate change, pollution, and resource depletion. As cities continue to embrace digital transformation and adopt data-driven approaches, the role of Fintech in promoting environmental sustainability will become increasingly crucial. By leveraging historical insights and embracing emerging technologies, cities can chart a path towards a more resilient, inclusive, and environmentally sustainable future. This Review underscores the importance of collaboration among stakeholders, including governments, businesses, academia, and civil society, to drive collective action and accelerate the transition towards sustainable smart cities.
 Keywords: Sustainable, Smart Cities, Fintech, Promoting, Environmental.

Evaluating implied urban nature vitality in San Francisco: An interdisciplinary approach combining census data, street view images, and social media analysis

Urban green spaces (UGS) are vital in modern cities, offering extensive health, social, and environmental benefits. However, traditional research methods primarily focus on UGS distribution and aggregation through 2D mapping, often neglecting the quality and vitality of urban natural environments. This limited approach hampers our full understanding of the complex issues and opportunities surrounding UGS. This study proposes a novel concept of Implied Urban Nature Vitality (IUNV) and evaluation framework that offers a comprehensive lens to understand better and evaluate the manifold human-urban-nature interactions in modern cityscapes. Based on our IUNV framework, an interdisciplinary investigation is conducted to show the distribution and population-level perceived IUNV in San Francisco by leveraging a triad of data sources: census, street-built environment, and social media data. Utilizing census data, we analyze socio-economic influences on UGS distribution and IUNV, including factors such as education, age demographics, income, and ethnicity. Street view imagery (SVI), analyzed with advanced image recognition algorithms, serves as a proxy for visual and physical aspects of IUNV, highlighting features like trees, sky, buildings, and roads. This analysis paints a granular picture of UGS's spatial distribution and physical attributes, facilitating an objective measure of IUNV. Subsequently, we analyze Flickr photos related to urban natural areas, examining their distribution and identifying thematic clusters that illuminate various aspects of UGS vitality. Lastly, we combine computer vision and manual review to define 12 IUNV themes from architecture and nature, eco-friendly gatherings, to cultural performance, exploring the relationship between the vitality clusters and the independent variables. The main findings are: (1) Macro-level factors (e.g., accessibility level, land use mix level, road density, population density, etc.) are the dominant variables influencing IUNV.; (2) Street view factors play key roles in IUNV. Through this holistic IUNV analysis, the study shed light on the complexities of urban green space planning and management, informing future urban development strategies towards greater vitality and, by extension, environmental and social sustainability.

Integrating Artificial Intelligence and Environmental Science for Sustainable Urban Planning

The rapid urbanization of modern cities presents significant challenges in sustainable development. To address these challenges, there is a growing integration of Artificial Intelligence (AI) and Environmental Science to enhance urban planning processes. This research aims to assess the impact and utility of AI techniques within the framework of Geographic Information Systems (GIS) for sustainable urban planning. Specifically, it investigates how AI-enhanced GIS tools can be employed to improve urban development strategies and enhance sustainability assessments. Employing Spatial Analysis with GIS, this study analyzes data on land use, population density, and environmental indicators across several metropolitan areas. The methodology incorporates machine learning algorithms to predict and simulate urban growth patterns, enabling the assessment of various urban planning scenarios. The findings reveal that AI-enhanced GIS tools significantly improve the precision of development forecasts and sustainability assessments. These tools facilitate more informed decision-making in urban planning by enabling precise predictions about urban expansion and its environmental impacts. The integration of AI with environmental science not only enhances the efficiency of urban planning processes but also contributes to the resilience and sustainability of urban environments. The study provides urban planners and policymakers with advanced tools to forecast and mitigate the environmental impacts of urbanization, thereby setting a benchmark for future studies in the realm of sustainable urban planning. This research demonstrates the practical application of AI in enhancing the capabilities of GIS for complex spatial analyses, contributing significantly to the field of urban planning.

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.

Examining the impact of urban management on regional innovation efficiency in China’s manufacturing industry with a focus on modern sustainable energy: utilizing a non-radial distance function method

This study examines the impact of urban management on the efficiency of regional innovation in China’s manufacturing industry, with a specific focus on modern sustainable energy. The research takes into consideration the regional diversity in innovation development across China’s three major regions and integrates common Frontier theory with the MSBM model. By employing a non-radial distance function approach, this study develops the MMSBMP model, incorporating various improved methods proposed by researchers. The Luenberger index methodology is utilized to assess the innovation efficiency of the national manufacturing industry from 2017 to 2021, enabling the identification of efficiency losses. The findings highlight significant disparities and opportunities for enhancing innovation efficiency across the three major regions, both at the common Frontier and the group Frontier. However, caution is advised due to the potential overestimation of regional Frontier efficiency values resulting from variations in technical reference datasets. Analysis of the Total Growth Rate (TGR) values reveals distinct development characteristics among the regions, with the eastern region exhibiting smaller extremes and the central and western regions displaying larger extremes. This comprehensive examination of China’s manufacturing industry emphasizes the influence of urban management. By investigating the consequences of urban management practices, this research provides insights into the relationship between urban development strategies and the performance, efficiency, and sustainable growth of the manufacturing industry. The study highlights the significance of urban management in shaping regional innovation efficiency and emphasizes the continual improvement of the industry’s innovation performance in the context of modern sustainable energy.

Maximising CO2 Sequestration in the City: The Role of Green Walls in Sustainable Urban Development

Environmental issues are a pressing concern for modern societies, and the increasing levels of atmospheric CO2 have led to global warming. To mitigate climate change, reducing carbon emissions is crucial, and carbon sequestration plays a critical role in this effort. Technologies for utilising CO2 can be divided into two major categories: direct use and conversion into chemicals and energy, and indirect use as a carbon source for plants. While plants’ ability to absorb and store CO2 makes them the best CO2 sink, finding suitable urban areas for significant green spaces is a challenge. Green walls are a promising solution, as they require less land, provide more ecosystem services than horizontal systems do, and can contribute to reducing environmental problems. This study evaluates the conceptual potentials and limitations of urban biomass circulation in terms of energy production, food production, and CO2 consumption, focusing on growth-promoting bacteria, urban agriculture, and vertical systems. The aim of this research is discovering new methods of carbon sequestration using multi-purpose green walls to achieve sustainable urban development and CO2 reduction strategies to contribute to a more sustainable future.

Urban population density and energy conservation: Empirical evidence from 276 cities in China

Reducing urban energy consumption is a crucial step towards achieving sustainable urban development. Urban energy plays a fundamental role in urban development, and while previous studies have examined the relationship between population size and energy conservation, the impact of increasing population density on per capita energy consumption (PCEC) remains unclear. To achieve urban energy conservation in China, it is vital to comprehend this significant relationship. This study constructs a spatial regression model to examine the relationship between population density and PCEC using 9 years of balanced panel data from 276 cities to fill a gap in the literature. The results of spatial autocorrelation indicate a significant negative relationship and heterogeneity between population density and PCEC. The results of spatial regression show that for every 1% increase in population density, there is a subsequent increase in PCEC of 0.074%. Our findings suggest that lower PCEC correlation is associated with higher urban population density. This study can be a reference for policymakers seeking new energy conservation strategies for urban development.

Mitigating air and noise pollution through highway capping: The Bundang-Suseo Highway Cap Project case study

Highways, while vital for transportation, often lead to heightened air and noise pollution, adversely affecting nearby communities. This study delves into the effectiveness of highway capping, a sustainable urban development strategy, in addressing these environmental challenges, with a specific focus on the Bundang-Suseo Highway in South Korea. This study employed a multifaceted approach, incorporating on-road monitoring, in situ measurements, and vertical assessments using UAVs. Following the cap's installation, the area experienced more stable pollutant levels, marking a notable shift from the previously fluctuating conditions heavily influenced by the highway. In-depth in situ monitoring near the cap revealed significant reductions in noise and pollutants like UFP and BC. Furthermore, UAV monitoring captured these changes in pollutant levels at different altitudes. Notably, the installation of the highway cap led to increased PM2.5, PM10, and NO2 levels at ground level, but a decrease above the cap, emphasizing the critical importance of intentional highway cap design in enhancing urban air quality and reducing exposure to harmful pollutants. This research yields invaluable insights for urban planners, health authorities, and policymakers, aiding the precise identification of pollution-prone areas and advocating for improved highway cap design to enhance urban environments.

Analysis of Urban Congestion Traceability: The Role of the Built Environment

Analyzing the factors influencing traffic congestion is essential for urban planning and coordinated development. Previous research frequently focuses on the internal aspects of traffic systems, often overlooking the impact of external factors on congestion sources. Therefore, this study utilizes a geospatial dataset and mobile signaling data, firstly applying the Fuzzy C-Means (FCM) algorithm to identify congested roads of different levels and trace the localization of travelers’ origins on regional congested roads. Furthermore, it employs the LightGBM method to study the influence of the built environment of various congestion sources on network-level congestion. The findings are as follows: (1) There is a positive correlation between traffic congestion and geographical location, with congestion predominantly caused by a few specific plots and demonstrating a concentrated trend in city centers. (2) Residential population density is the most critical factor, accounting for over 12% of the congestion contribution, followed by road density and working population density. (3) Both residential and working population densities show a non-linear positive correlation with congestion contribution, while the mixture of land use displays a non-linear V-shaped influence. Additionally, when residential population density is between 8000 and 11,000, it notably exacerbates congestion contribution. Significantly, by emphasizing land use considerations in traffic system analysis, these findings illuminate the intricate linkages between urban planning and traffic congestion, advocating for a more comprehensive approach to urban development strategies.

Predicting building energy consumption in urban neighborhoods using machine learning algorithms

Assessing building energy consumption in urban neighborhoods at the early stages of urban planning assists decision-makers in developing detailed urban renewal plans and sustainable development strategies. At the city-level, the use of physical simulation-based urban building energy modeling (UBEM) is too costly, and data-driven approaches often are hampered by a lack of available building energy monitoring data. This paper combines a simulation-based approach with a data-driven approach, using UBEM to provide a dataset for machine learning and deploying the trained model for large-scale urban building energy consumption prediction. Firstly, we collected 18,789 neighborhoods containing 248,938 buildings in the Shanghai central area, of which 2,702 neighborhoods were used for UBEM. Simultaneously, building functions were defined by POI data and land use data. We used 14 impact factors related to land use and building morphology to define each neighborhood. Next, we compared the performance of six ensemble learning methods modeling impact factors with building energy consumption and used SHAP to explain the best model; we also filtered out the features that contributed the most to the model output to reduce the model complexity. Finally, the balanced regressor that had the best prediction accuracy with the minimum number of features was used to predict the remaining urban neighborhoods in the Shanghai central area. The results show that XGBoost achieves the best performance. The balanced regressor, constructed with the 9 most contributing features, predicted the building rooftop photovoltaics potential, total load, cooling load, and heating load with test set accuracies of 0.956, 0.674, 0.608, and 0.762, respectively. Our method offers an 85.5%-time advantage over traditional methods, with only a maximum of 22.75% of error.

Linking geodiversity and geosystem services to human well-being for the sustainable utilization of the subsurface and the urban environment.

Because the functions of the subsurface are hidden from view, its important role in society is often ignored or taken for granted. The subsurface is, however, an essential part of the global ecosystem with important contributions to human well-being. Geodiversity is an important characteristic in this respect. Material supply is the more obvious role of the subsurface with projections of a doubling of global material use in 2060 as compared to 2017. Moreover, creating underground spaces and infrastructure are gaining importance in the urban environment. The main reason for the inadequate protection of geodiversity is the lack of a comprehensive and integrative framework. Linking socio-economic activities to biophysical system characteristics of the subsurface is facilitated by the geosystem services approach. Sustainable urban development strategies require including geodiversity in decision-making on human well-being and setting conditions for land use change. Spatial plans and decisions on the use of natural endowments should look at processes over much longer timeframes. In this paper, we explore the links between human well-being and the subsurface with an emphasis on the role of geodiversity. We set out a methodological framework and describe possible long term three-dimensional land use planning consequences for sustainable utilization of the subsurface. This article is part of the Theo Murphy meeting issue 'Geodiversity for science and society'.