Sustainable Development Of Cities Research Articles

Environmental shadows in the age of progress: The toll of economic globalization on China's climate

This study investigates the impact of economic globalization on China's climate using city-level data from 2005 to 2019. This study employs the Spatial Durbin Model (SDM) to reveal an inverted U-shaped relationship between globalization and climate impact, with carbon emissions initially increasing due to scale and composition effects. Surprisingly, the technology effect has limited effectiveness, particularly in second and third-tier cities. Another significant finding of this study is the observed positive correlation between carbon emissions in the previous and current periods. However, this trend leads to lower carbon emissions in neighbouring regions. The results of this study call for technology-focused policies and international collaborations to promote sustainable development in Chinese cities.

Spatial–temporal variation and correlation analysis of ecosystem service values and ecological risks in winter city Shenyang, China

Ecosystems in winter cities are complex and fragile, experiencing significant changes due to climate variations and human construction activities. Previous studies on the assessment of overall ecosystem service value (ESV) and ecological risk index (ERI) in winter cities are scarce. In this study, we constructed ESV and ERI measurement models using land use data in 2000, 2010, and 2020 using the improved value per unit area factor method and the landscape pattern index method, respectively, to reveal their spatial and temporal change characteristics. Geographic detectors were used to explore the driving roles of natural and artificial factors on the changes of ESV and ERI. The combination in ESV and ERI can then provide a more quantitative and accurate basis for policy decisions, identify priority areas for urban ecological restoration, and reduce the risk to ecosystems. The results of the study show that the total ESV of Shenyang city decreased from 273.97 × 108 CNY to 270.38 × 108 CNY during 2000–2020. Although the decrease is not large, the ESV changes structurally with the advancement of urbanization. During the 20 years, the construction land with the lowest ecological service function continues to expand, increasing by 354 km2, the grassland decreased by 215.9 km2, and the arable land decreased by 196.6 km2. The ecological service function of the water area is the strongest, with an increase of 51.3 km2 in the water area, ensuring that there is no significant decline in ESV. The size of the ERI is Very high, High, and Medium value zones remained relatively stable, while the size of the Very Low-value zone decreased by 12.78% and the size of the Low-value zone increased by 13.21%. The interaction factors that contributed most to the changes in ESV and ERI were annual evapotranspiration (EVP)/ Normalized Difference Vegetation Index (NDVI) and Annual sunshine hours (SSD)/ Digital Elevation Model (DEM) , respectively. There was a spatial correlation between ESV and ERI. The areas with the highest ESV supply capacity and at the same time facing severe ecological risks to the landscape pattern are distributed in the northeastern hilly lands. This area should be prioritized to develop planning and control measures to prevent further erosion of forest lands and grasslands and reduce ecological risks. These results provide a theoretical basis for ensuring ecological security and sustainable development in winter cities.

Time-series China urban land use mapping (2016–2022): An approach for achieving spatial-consistency and semantic-transition rationality in temporal domain

The global urbanization trend is geographically manifested through city expansion and the renewal of internal urban structures and functions. Time-series urban land use (ULU) maps are vital for capturing dynamic land changes in the urbanization process, giving valuable insights into urban development and its environmental consequences. Recent studies have mapped ULU in some cities with a unified model, but ignored the regional differences among cities; and they generated ULU maps year by year, but ignored temporal correlations between years; thus, they could be weak in large-scale and long time-series ULU monitoring. Accordingly, we introduce an temporal-spatial-semantic collaborative (TSS) mapping framework to generating accurate ULU maps with considering regional differences and temporal correlations. Firstly, to support model training, a large-scale ULU sample dataset based on OpenStreetMap (OSM) and Sentinel-2 imagery is automatically constructed, providing a total number of 56,412 samples with a size of 512 × 512 which are divided into six sub-regions in China and used for training different classification models. Then, an urban land use mapping network (ULUNet) is proposed to recognize ULU. This model utilizes a primary and an auxiliary encoder to process noisy OSM samples and can enhance the model's robustness under noisy labels. Finally, taking the temporal correlations of ULU into consideration, the recognized ULU are optimized, whose boundaries are unified by a time-series co-segmentation, and whose categories are modified by a knowledge-data driven method. To verify the effectiveness of the proposed method, we consider all urban areas in China (254,566 km2), and produce a time-series China urban land use dataset (CULU) at a 10-m resolution, spanning from 2016 to 2022, with an overall accuracy of CULU is 82.42%. Through comparison, it can be found that CULU outperforms existing datasets such as EULUC-China and UFZ-31cities in data accuracies, spatial boundaries consistencies and land use transitions logicality. The results indicate that the proposed method and generated dataset can play important roles in land use change monitoring, ecological-environmental evolution analysis, and also sustainable city development.

Design and Enhancement of a Fog-Enabled Air Quality Monitoring and Prediction System: An Optimized Lightweight Deep Learning Model for a Smart Fog Environmental Gateway.

Effective air quality monitoring and forecasting are essential for safeguarding public health, protecting the environment, and promoting sustainable development in smart cities. Conventional systems are cloud-based, incur high costs, lack accurate Deep Learning (DL)models for multi-step forecasting, and fail to optimize DL models for fog nodes. To address these challenges, this paper proposes a Fog-enabled Air Quality Monitoring and Prediction (FAQMP) system by integrating the Internet of Things (IoT), Fog Computing (FC), Low-Power Wide-Area Networks (LPWANs), and Deep Learning (DL) for improved accuracy and efficiency in monitoring and forecasting air quality levels. The three-layered FAQMP system includes a low-cost Air Quality Monitoring (AQM) node transmitting data via LoRa to the Fog Computing layer and then the cloud layer for complex processing. The Smart Fog Environmental Gateway (SFEG) in the FC layer introduces efficient Fog Intelligence by employing an optimized lightweight DL-based Sequence-to-Sequence (Seq2Seq) Gated Recurrent Unit (GRU) attention model, enabling real-time processing, accurate forecasting, and timely warnings of dangerous AQI levels while optimizing fog resource usage. Initially, the Seq2Seq GRU Attention model, validated for multi-step forecasting, outperformed the state-of-the-art DL methods with an average RMSE of 5.5576, MAE of 3.4975, MAPE of 19.1991%, R2 of 0.6926, and Theil's U1 of 0.1325. This model is then made lightweight and optimized using post-training quantization (PTQ), specifically dynamic range quantization, which reduced the model size to less than a quarter of the original, improved execution time by 81.53% while maintaining forecast accuracy. This optimization enables efficient deployment on resource-constrained fog nodes like SFEG by balancing performance and computational efficiency, thereby enhancing the effectiveness of the FAQMP system through efficient Fog Intelligence. The FAQMP system, supported by the EnviroWeb application, provides real-time AQI updates, forecasts, and alerts, aiding the government in proactively addressing pollution concerns, maintaining air quality standards, and fostering a healthier and more sustainable environment.

Quantifying city freight mobility segregation associated with truck multi-tours behavior

Freight mobility segregation, a phenomenon like residential social segregation, is a crucial aspect of city landscapes that influences the livability and sustainability of cities. However, there is a deficiency in understanding the intrinsic complexity of freight mobility segregation, particularly regarding the micro-level truck behaviors. In this study, we develop a new approach to assess the degree of freight mobility segregation within cities by leveraging large-scale truck GPS data in Chinese cities. The analysis indicates the existence of freight mobility segregation in cities, where certain groups of trucks serve high-demand areas, while another group of trucks serves low-demand areas. The activity spaces of distinct truck groups are largely non-overlapping or segregated. To uncover the correlations between mobility segregation and truck operational patterns, we introduce two metrics to characterize truck multi-tours behavior, focusing on tour pattern predictability and activity explorability. By employing freight point-of-interest (POI) data, we further reveal the influence of local economic structures and industrial compositions on mobility segregation in cities. These findings enrich our understanding of the dynamics of city freight systems, offering implications for improving logistics efficiency and fostering sustainable city development.

Uncovering the impacts of LUCC on ecological connectivity in suburban open-pit mining concentration areas: A pattern collection of changing relationships between ecological resistance and ecological network elements

The suburban open-pit mining concentration area couples two human activities, urban sprawl and open-pit mining, while destruction of surface vegetation and ecological restoration coexist. This has also resulted in complex land use shifts that significantly affect regional ecological connectivity. However, the impact of such complex Land use/land cover changes (LUCC) on ecological connectivity in this region is not well understood Therefore, this study takes the suburban open-pit mining concentration area of Jinan City as a case study and uses Land use/land cover (LULC) data from the years 2000, 2010, and 2020 to simulate the spatio-temporal evolution of ecological networks (ENs). Subsequently, a pattern collection of changing relationships was proposed that relate changes in ecological resistance to changes in EN elements, addressing the dynamic transmission process from "spatial field" to "spatial structure entity". Based on this approach, GeoDetector is employed to reveal the response extent of EN elements to LUCC. The results indicate: (1) In the past 20 years, the area of construction land has increased by 100 %, while mining land has increased by 69 %. This significant increase in LUCC has led to a notable enhancement in ecological resistance in the study area, driving the generation of 20 % more ecological pinch points and barriers. Additionally, it has resulted in a twofold increase in both the number and length of level 1 ecological corridors. (2) The dominant status and structure of grassland, agriculture land, and woodland remain relatively stable; however, the interflow among them has caused approximately 25 % of the ecological corridors to undergo changes such as disappearance and path alteration. (3) The implementation of ecological protection and restoration has led to a continuous increase in the area of ecological sources, particularly with the mining rehabilitation increasing by 32.7 times compared to 2000, thereby driving gradual improvement in 25 % of the ecological network elements. (4) Although the quantity and length of ecological corridors in the study area remain relatively stable, evaluation results indicate that its spatial structure is gradually deteriorating, with ecological connectivity decreasing by 7.7 %. This study highlights the importance of restoration of ecological processes in sub-urban open-pit mining concentration areas. The study results can provide a scientific basis for ecological restoration and sustainable development in resource-based cities.

MGSFformer: A Multi-Granularity Spatiotemporal Fusion Transformer for air quality prediction

Air quality spatiotemporal prediction can provide technical support for environmental governance and sustainable city development. As a classic multi-source spatiotemporal data, effective multi-source information fusion is key to achieving accurate air quality predictions. However, due to not fully fusing two pieces of information, classical deep learning models struggle to achieve satisfactory prediction results: (1) Multi-granularity: each air monitoring station collects air quality data at different sampling intervals, which show distinct time series patterns. (2) Spatiotemporal correlation: due to human activities and atmospheric diffusion, there exist correlations between air quality data from different air monitoring stations, necessitating the consideration of other air monitoring stations' influences when modeling each air quality time series. In this study, to achieve satisfactory prediction results, we propose the Multi-Granularity Spatiotemporal Fusion Transformer, comprised of the residual de-redundant block, spatiotemporal attention block, and dynamic fusion block. Specifically, the residual de-redundant block eliminates information redundancy between data with different granularities and prevents the model from being misled by redundant information. The spatiotemporal attention block captures the spatiotemporal correlation of air quality data and facilitates prediction modeling. The dynamic fusion block evaluates the importance of data with different granularities and integrates the prediction results. Experimental results demonstrate that the proposed model surpasses 11 baselines by 5% in performance on three real-world datasets.

Multi-Scale Dynamics and Spatial Consistency of Economy and Population Based on NPP/VIIRS Nighttime Light Data and Population Imagery: A Case Study of the Yangtze River Delta

Population and economy are crucial factors contributing to regional disparities. Studying the patterns and relationships between these two elements is essential for promoting sustainable development in regions and cities. This study constructs multi-scale geographic concentration indices and inconsistency indices, utilizing NPP/VIIRS and LandScan data to quantitatively analyze the spatial pattern changes of population and economy in the Yangtze River Delta across various spatial scales, revealing the matching relationships between population and economic elements within cities. The results indicate that the economy in the Yangtze River Delta is spreading outward from the core areas, with the average population–nightlight inconsistency index decreasing from 1.57 to 1.33. This suggests that the imbalance between population and economy within the urban agglomeration is gradually improving, consistent with trends observed in statistical survey data. Within individual cities, there is a noticeable spatial mismatch between population and nightlight intensity, with the population primarily concentrated in urban core areas. As urban spaces expand, the areas where population concentration is significantly lower than nightlight concentration are gradually diminishing. By 2022, the land area where population and economic concentration are coordinated within the Yangtze River Delta urban areas increased from 9.13% to 16.24%. Population concentration in these coordinated regions rose from 11.33% to 16.33%, while nightlight concentration increased from 9.98% to 13.63%. The improved geographic concentration and inconsistency indices are effective indicators for multi-scale monitoring of population and economic spatial changes. The integration of NPP/VIIRS nighttime light data and LandScan data provides an effective method for uncovering different spatial patterns of population and socio-economic element aggregation in urban structures. This can offer insights for promoting sustainable regional and urban development.

Research on automatic identification of coal mining subsidence area based on InSAR and time series classification

Large-scale mining of coal resources has caused surface subsidence, threatening the ecological environment and surface safety and hindering the sustainable development of coal resource-based cities seriously. In order to identify coal mining subsidence area more accurately and quickly, a novel method for automatic identification of coal mining subsidence area was proposed in this study, which is based on InSAR technology and time series classification algorithm. The method comprehensively considered the quality of data acquisition and the degree of automation of algorithm implementation, realizing automatic identification of large-space coal mining subsidence area in the case of small samples. Firstly, the unclassified subsidence dataset is constructed by SAR data around time series combined with InSAR technology. Secondly, the degree of change of time series subsidence is used as the basis of classification, and it is divided into two categories: regular type of coal mining subsidence and irregular type of non-coal mining subsidence. Finally, Dynamic Time Warping (DTW) algorithm is used to calculate the similarity distance between the time series curves of each image element of the subsidence dataset to be tested and the standard coal mining subsidence type curves, which aim at identifying the coal mining subsidence area. Through utilizing the multitude filtering algorithm to filter the recognition results, the automatic recognition results of coal mining subsidence area is finally obtained. The results show that this method can still be used to efficiently identify and circle coal mining subsidence area with only 640 samples, and the average Overall Accuracy (OA) and Kappa Coefficient (KC) of coal mining subsidence area are 0.91 and 0.83, respectively. The method can not only identify the existing and potential subsidence area in the mining area, but provide technical support for ecological environment restoration and early warning of subsidence damage.

Interpretation of the imaginary of farmers about the products obtained through urban agriculture

The present investigation was carried out with the objective of identifying the factors that affect the adoption of urban agriculture practices in the municipality of Florencia, Caquetá. To achieve this objective, an exploratory study was carried out that involved the collection and analysis of data through surveys and interviews with Mr. José. The result of this research was analyzed in detail on the interpretation of the farmers' imaginary about the products obtained through urban agriculture, as well as recommendations to promote their adoption and improve their impact on the local population. So that is why urban agriculture is a useful tool to promote the sustainable development of cities. This is because it offers a viable solution to satisfy the growing demand for food and self-consumption in the urban area, discovering food sovereignty in addition to creating important social, economic and environmental benefits

Spatial and temporal variation of PM2.5 and the influence of vegetation in the Yangtze River Delta region

With rapid urbanisation in China, PM2.5 has become a limiting factor for the sustainable development of cities. Taking the Yangtze River Delta as the experimental area, this study analysed the spatial and temporal changes of PM2.5 concentrations from 2001 to 2020. It also examined the variations, dispersion, and correlation with NDVI of PM2.5 concentrations in different vegetation zones at different temporal and spatial scales. The results showed that: (1) The PM2.5 concentration in the Yangtze River Delta showed an overall decreasing trend during 2001–2020, and the change was divided into two phases, starting with an increasing phase and entering a decreasing phase after 2013. (2) In terms of spatial distribution, PM2.5 concentrations in the Yangtze River Delta show a pattern of low in the south and high in the north, with the spatial focus shifting to the north over time. There is a concentration of high levels of particulate matter in the Hefei-Nanjing-Wuxi area. (3) The effect of natural vegetation on the reduction and stabilization of atmospheric particulate matter concentration is better than that of artificial vegetation. (4) Needleleaf forests, broadleaf forests, and shrubs in natural vegetation are more capable of reducing and stabilizing atmospheric particulate matter than grasses. The study can provide a reference for regional air pollution control and regional plant system construction.

The impact of sustainable development policy for resource-based cities on green technology innovation: Firm-level evidence from China

The sustainable development policy targeting resource-based cities (RBC) is a critical measure initiated by China's government to transform these cities. Green technology innovation (GTI) has surfaced as a pivotal route in achieving sustainable development in RBCs. However, whether implementing this policy can induce GTI remains underexplored. To fill this gap, exploiting the National Plan for Sustainable Development of Resource-based Cities (2013–2020) (NPSDRC) as a quasi-natural experiment, we employ the difference-in-differences (DID) method to examine the influence of NPSDRC on corporate GTI and potential mechanisms. We observe that NPSDRC significantly reduces the GTI of RBC enterprises. Mechanism analysis reveals that NPSDRC escalates environmental governance costs and decreases R&D investment, impeding corporate GTI. Meanwhile, our findings suggest that providing more bank loans and tax incentives could weaken this adverse effect on corporate GTI. Heterogeneity results indicate that non-SOEs, enterprises with weak financing capabilities, enterprises with long-sighted vision, and high energy consumption enterprises are more susceptible to the influence of NPSDRC. This study offers empirical evidence for countries seeking to employ policies to drive the transformation of RBCs.

Elemental evolution characteristics and influencing factors of green infrastructure network in karst mountain cities: a case study of Qianzhong urban agglomeration in Southwest China

BackgroundThe urban green infrastructure (GI) network is an important conduit for ecological flows and plays a crucial role in improving regional habitats, especially in karst areas that are highly ecologically fragile and sensitive. However, the existing research only focuses on the construction of GI network in karst mountain cities, and the evolution characteristics of its elements and driving mechanism are not clear, which is of great significance for guiding urban land use planning and comprehensively improving the quality of the ecological environment. In view of this, this study took Qianzhong urban agglomeration as the study area, based on multi-source data, and identified ecological sources through ecological resilience analysis. Considering the special geographic environment, the rock exposure rate factor was added to correct the resistance surface, and the minimum cumulative resistance (MCR) and gravity model were coupled to extract the GI network. The complex network topology characterization parameter was introduced to assess the spatial and temporal variations of ecological sources and corridors. Finally, the geographical detector was used to identify the dominant influencing factors and interactions of the spatial distribution of the GI network.ResultsThe results showed that from 2000 to 2020, the condition of GI network elements in the study area presented a decreasing and then an increasing trend. The ecological sources or corridors in highly urbanized areas were critical for ecological flow transport and the overall structural stability of the GI network. The influence of natural factors on the spatial distribution of the GI network gradually weakened, and the influence of human factors continuously increased. The spatial distribution of the GI network was influenced by multiple factors, and the interaction between all the factors was enhanced, which gradually changed from the interaction of natural factors to the interaction of human factors during the study period.ConclusionsThe research results will provide scientific references for the construction of an ecologically safe environment and sustainable development of karst mountain cities.

Research on the Application of Geographic Information Systems in the Construction of Smart Cities

With the continuous deepening of globalization and informatization, the construction of smart cities is gradually becoming a core strategy in the process of urban modernization. In this strategy, Geographic Information Systems (GIS) play a crucial technical support role. This article adopts a case study method, taking the construction of a smart city in Nanjing as the specific research object, and deeply explores the practical application of GIS in key fields such as urban planning, environmental protection, and traffic management. Through comprehensive analysis of these examples, this article systematically summarizes the significant achievements of GIS technology in improving urban management efficiency, optimizing decision support systems, enhancing public service levels, and promoting sustainable urban development. The research results indicate that GIS technology provides solid data support and decision-making support for the core areas of cities through precise integration and analysis of geographic information. GIS technology not only optimizes the layout of urban infrastructure, but also significantly enhances the city's response to emergency events through real-time monitoring and dynamic simulation, while promoting efficient allocation and utilization of urban resources. The research conclusion points out that, GIS technology is the cornerstone of smart city construction, greatly enhancing the scientific and forward-looking nature of urban management. The effective application of GIS has opened up new avenues for intelligent and refined urban management, significantly improving the quality of life of residents and providing solid technical support for the sustainable development of cities.

How to achieve sustainable development of smart city: a comprehensive bibliometric analysis

How to achieve sustainable development of smart city: a comprehensive bibliometric analysis

How building arrangements can improve outdoor thermal comfort and indoor sunlight?

In the context of frequent extreme hot weather conditions, high-dense urban areas posed more life-threatening challenges. Such as university dormitories, as important residential spaces, have received very little attention. Moreover, how building arrangements can simultaneously impact internal and external environments remain poorly understood. To bridge this gap, student residential areas of Jiangsu University in Zhenjiang, China were considered as a case study, with the aim of optimal building arrangements that can simultaneously improve the outdoor thermal comfort (OTC) and indoor sunlight (ISL). A combination of on-site measurement and numerical models were employed to simulate various scenarios with different building orientations, building heights and layouts. The result revealed that: first, the building orientation emerged as a dominant factor influencing both OTC and ISL, with the optimal value identified as SE-30°. Second, in the context of extremely hot weather conditions, increasing building height or floor-area ratio contributed to improved OTC. Finally, the building arrangement with inconsistent heights of north-to-south descending order, exhibited a notable reduction in hot outdoor spaces by 18.6%, and attributed to enhanced airflow. The findings provide clear guidelines for improved strategies at the detailed urban design scale, and can further contribute to the development of sustainable cities.

MonuNet: a high performance deep learning network for Kolkata heritage image classification

Kolkata, renowned as the City of Joy, boasts a rich tapestry of cultural heritage spanning centuries. Despite the significance of its architectural marvels, accessing comprehensive visual documentation of Kolkata's heritage sites remains a challenge. In online searches, limited imagery often fails to provide a detailed understanding of these historical landmarks. To address this gap, this paper introduces MonuNet, a high-performance deep-learning network specifically designed for the classification of heritage images from Kolkata. The development of MonuNet addresses the critical need for efficient and accurate identification of Kolkata's architectural marvels, which are significant tangible cultural heritages. The dataset used to train MonuNet is organized by heritage sites, each category within the dataset represents distinct sites. It includes images from 13 prominent heritage sites in Kolkata. For each of these sites, there are 50 images, making it a structured collection where each category (heritage site) is equally represented. The proposed network utilizes a unique architecture incorporating a Dense channel attention module and a Parallel-spatial channel attention module to capture intricate architectural details and spatial relationships within the images. Experimental evaluations demonstrate the superior performance of MonuNet in classifying Kolkata heritage images with an accuracy of 89%, Precision of 87.77%, and Recall of 86.61%. The successful deployment of MonuNet holds significant implications for cultural preservation, tourism enhancement, and urban planning in Kolkata, aligning with the United Nations Sustainable Development Goals (SDGs) for sustainable city development. By providing a robust tool for the automatic identification and classification of heritage images, MonuNet promises to enrich online repositories with detailed visual documentation, thereby enhancing accessibility to Kolkata's cultural heritage for researchers, tourists, and urban planners alike.Graphical

Assessment of green sustainable development in plateau lakeside cities

IntroductionGreen sustainable development is an important part of the United Nations Sustainable Development Goals (SDGs) and China’s ecological civilization construction.MethodsThis paper combines the characteristics of the development of lakeside cities, gives full play to the advantages of modern remote sensing technology to collect the indicators related to the green sustainable development of lakeside cities, adopts the Analytic Hierarchy Process (AHP) and the CRiteria Importance Through Inter-criteria Correlation (CRITIC) subjective and objective weighting methods and the difference coefficient method to determine the weights of the indicators, evaluates the green sustainable development of a typical lakeside city of Kunming for more than 30 years, and analyzes the influencing factors using the gray degree of correlation.ResultsThe results show the following: (1) From 1990 to 2021, the level of green sustainable development in the urban area around Dianchi Lake fluctuated in stages, with an overall upward trend, and the green development index increased from 0.25 in 1990 to 0.5 in 2021. (2) The coefficient of variance determined by the optimization model can be a better compromise between the objective and subjective weights of the indicators, and the massive occupation of wetlands and arable land around the lake in the urban expansion has become a major factor in the development of the city, with a combined weight of 10.30% and 8.79%, respectively. (3) The urban population and economic scale are currently the main drivers of the green and sustainable development of a lakeside city, with a correlation of 0.843 and 0.769, respectively.DiscussionThe growth of urban population and economic scale plays a pivotal role in driving the green and sustainable development of lakeside cities, while the potential impact of tourism development should also be considered.

Wind Farms’ Location and Geographical Proximity as a Key Factor in Sustainable City Development: Evidence from Poland

In this study, the potential impact of wind farm locations on the sustainable development of cities in Poland was evaluated, considering the availability of wind-generated electricity. We analyzed 37 cities in Poland with populations over 100,000. Research indicates that wind farms located 30–80 km from large cities perform best in terms of generating capacity, while greater distances increase transmission costs and energy losses. In Poland, wind farms are primarily situated in the northwestern regions, posing challenges for energy transmission to the southern cities, which are the main centers of energy consumption. The findings show that wind farms with the highest generating capacity are generally about 50 km from major cities. Key factors influencing wind farm locations include technical criteria, economic feasibility, environmental impact, public opinion, and the availability of transmission networks. Sustainable development of wind farms requires strategic cooperation between urban and rural municipalities, joint spatial planning, coordinated land acquisition, and the exchange of know-how.

Identification of indicators of urban affordable housing location choice for low-income households: a Delphi analysis

ABSTRACT The rapid growth of urbanization has resulted in an unprecedented housing crisis, particularly in developing nations. Tackling housing challenges is essential for the sustainable development of cities. One of the prime challenges in this domain is the identification of suitable locations for developing affordable housing projects. This research aims to identify the indicators that affect the choice of affordable housing locations for low-income households. Through exploration of relevant literature, a comprehensive list of 48 indicators was identified and classified into five categories; neighborhood embedded characteristics, urban characteristics, economic context, and social and housing characteristics. The Delphi method was adopted to gain consensus on the parameters identified. To encompass diverse viewpoints, experts were chosen from five metropolitan cities, including Jaipur, Lucknow, Patna, Mumbai, and Chennai, representing five geographical zones across India; criteria for selection included expertise in affordable housing, low-income housing, or sustainable development. 36 indicators gained consensus through a two-round Delphi survey with 0.71 as the value of Kendall’s coefficient of Concordance. The parameters of mobility, high employment opportunities, and proximity to various facilities were found to be significant when considering location choice. The indicators determined can help identify the relative locations of affordable housing development in urban areas.