This study explores the relationships among highway expansion, transport energy consumption, and carbon emissions in Europe. Using panel data from 30 countries between 2011 and 2020, it applies fixed-effects quantile regression, supported by panel stationarity, cross-sectional dependence, and cointegration tests, together with panel Granger causality analysis. The findings show that highway expansion is positively associated with transport energy demand at the lower conditional quantiles, while negative associations emerge across most of the remaining distribution. These patterns are consistent with the possibility that the balance between induced-demand and network-efficiency mechanisms differs across the distribution. Highway expansion is also negatively associated with carbon emissions across most quantiles. The study further identifies varying patterns of price elasticity in transport energy demand across the conditional distribution. These results underline the importance of accounting for heterogeneity when evaluating infrastructure–environment relationships and provide relevant evidence for transport policies concerned with energy efficiency and climate change mitigation.

In many regions of Türkiye, land use development is chosen and managed without consideration to land capability, which results in disinvestment and lowers ecological capacity. In order to support the local authority's decision-making process, this article presents an analytical method that uses individual suitability maps to determine if a region is suitable for developing various economic sectors. Using a Multi-Criteria Decision Analysis (MCDA) integrated with Geographic Information Systems (GIS), the method is made up of the Analytic Hierarchy Process (AHP), a deterministic and fuzzy logic approach, the latter two were used for the standardisation of the criteria maps. The land-use suitability study took into account a number of criteria, such as geophysical characteristics, accessibility, built-up area and infrastructure, vegetation, and green and blue infrastructure. When compared to alternatives based on conventional approaches, the integration of fuzzy logic, AHP, and GIS is an advanced methodology that can enhance suitability evaluations.

High-density urban development is promoted by both global and local policies in response to socio-economic and environmental challenges since it increases mobility of different land uses, decreases the need for traveling, encourages the use of more energy-efficient buildings and modes of transportation, and permits the sharing of scarce urban amenities. It is therefore argued that increased density and mixed-use development are expected to deliver positive outcomes in terms of contributing to three pillars (social, economic, and environmental domains) of sustainability in the subject themes. Territorial quality of life (TQL)—initially proposed by the ESPON Programme—is a composite indicator of the socio-economic and environmental well-being and life satisfaction of individuals living in an area. Understanding the role of urban density in TQL can provide an important input for urban planning debates addressing whether compact development can be promoted by referring to potential efficiencies in high-density, mixed land use and sustainable transport provisions. Alternatively, low-density suburban development is preferable due to its benefits of high per capita land use consumption (larger houses) for individual households given lower land prices. There is little empirical evidence on how TQL is shaped by high-density versus low-density urban forms. This paper investigates this topic through providing an approach to spatially map and examine the relationship between TQL, residential expansion, and densification processes in the so-called NUTS2 (nomenclature of terrestrial units for statistics) regions of European Union (EU) member countries. The relative importance of each TQL indicator was determined through the entropy weight method, where these indicators were aggregated through using the subject weights to obtain the overall TQL indicator. The spatial dynamics of TQL were examined and its relationship with residential expansion and densification processes was analysed to uncover whether the former or the latter process is positively associated with the TQL indicator within our study area. From our regression models, the residential expansion index is negatively related to the TQL indicator, implying that high levels of residential expansion can result in a reduction in overall quality of life in the regions if they are not supported by associated infrastructure and facility investments.

Landslides can be considered one of the most severe natural hazards globally, and their management has a key role to inman safety. Landslide susceptibility maps can help the sustainable management of peri-urban areas such as determining the target areas of projects to develop landslide resistant areas, forest planning, infrastructure planning, and land use zoning. This study aims to analyse the relationship between landslide occurrence and its determinants in a peri-urban region, namely Taşlıdere Basin located in Güneysu District of Rize, Turkey, through providing both global and local regression models. To consider spatial non-stationarity, geographically weighted regression was used as a local model. It was found that the local model outperformed the global model in the estimation of landslide susceptibility determinants. The spatial analysis results indicated that almost all variables had heterogeneous variation over the study area. Therefore, this study provides a methodology for understanding the local dynamics of landslide occurrence.

Climate-related vulnerability indices are increasingly being utilized to enhance the creation of better disaster management strategies and to better understand and anticipate the effects of disasters related to climate change. This study evaluates the climate change vulnerability of the cities in Türkiye through focusing on their exposure, susceptibilities and adaptive capacities to climate change. Data from social, economic and environmental sub-indicators were assessed and most relevant indicators were aggregated with the goal of constructing a composite Climate Change Vulnerability Index (CCVI). The CCVI includes six forms of capital leading to socio-economic and environmental sustainability i.e. social, public utility and transport, economics, land cover, meteorology and atmospheric conditions, and natural disaster capitals, and will be assessed combining each of these forms of capitals and its three dimensions (exposure, susceptibility and adaptive capacity). Stakeholder-driven structured methodology that discovers and ranks context-relevant indicators and sets weights for aggregating indicator scores by using the Best-Worst method (BWM) and stepwise weight assessment ratio analysis (SWARA) method are utilised. The indicators are aggregated through application of the BWM and SWARA weights using a linear aggregation method. From BWM and SWARA, the highest weights were obtained for meteorological conditions and land cover which are more than 0.36 and 0.22, respectively. The lowest weights were assigned to social characteristics and economy both of which were smaller than 0.10. The findings indicated that the cities on the northern, western and southern coasts as well as the cities in south-east region are the most vulnerable to climate change. The construction of CCVI can be used as part of decision-making process to minimize hazards and exposure to risk of climate change for the cities of Türkiye.

A residential energy consumption model was estimated by using socio-economic characteristics, economic activities, mobility, land cover, natural hazards, governance, energy, air quality, and green economy variables for the EU-27 and UK. Regional variations of the energy consumption were also investigated through focusing on European regional typologies including urban, intermediate, and rural regions. The residential energy consumption model was estimated by using spatial econometric approaches as well as specific regression models were estimated for the urban, intermediate, and rural regions. The key variables used in regression analysis were selected according to their importance using the Random Forest (RF) classification method. The results from the regressions confirm that socio-economic, environmental, governance, technology, and natural hazards related variables explain residential energy consumption in Europe. The variations of sign and coefficients of the variables according to different regional typologies were also uncovered.

Poverty and inequality are the outstanding challenges in both developing and developed countries in the globe. Using Suomi National Polar-orbiting Partnership (NPP)-Visible Infrared Imaging Radiometer Suite (VIIRS) nighttime light (NTL) images and socio-economic data from administrative sources, this chapter focuses on the association between nighttime lights and economic activities with an aim of computing regional income inequality indices for the year 2015 in Turkey. Gini, the Atkinson and Theil statistics were used to establish regional inequality indices using both NTL and statistics data. The findings indicated that urban NTLs are strongly correlated with economic activity while the correlation is much weaker regarding rural nightlights and agricultural output. It can be noted that there was increasing regional inequality in north-west, south, and south-east regions whereas regional equality was more homogeneously distributed. The results indicated that NPP-VIIRS nightlight data can help to perform regional inequality assessments for the urban areas in Turkey.

Valuation of the economic, social and environmental impacts of air pollution has become crucial for the benefit-cost analysis of pollution restriction strategies, which serve as a foundation for establishing priorities for action. This paper focuses on the estimation of total external costs caused by road transport-related air pollutants using an integrated evaluation methodology combining air quality modelling, engineering science and economics. The results showed that total external costs of air pollution in Turkey in 2018 ranges between 37,500 euro that is computed for CO emissions and 2,686 million euro computed as an upper limit for NOx emissions. Regarding the social costs of CO2 emissions, the values range between 31 million euro and 1,427 million euro, the former represents the low value estimate while the latter is the high value estimate. The findings indicate that the impact of emissions from road transport on environment and society can be substantial in Turkey. Therefore, some regulations are necessary to reduce transport emissions and to sustain socio-economic welfare.

Land use change can have adverse impact on society and environment and therefore this puts enormous pressure on governments. Accurate estimates of future urban expansion are essential for sustainable growth and the preservation of the environment. This article examines the land use changes for urban uses, and further applies different methods for the projection of residential and industrial/commercial land uses in the selected case study area i.e. NUTS3 (nomenclature of terrestrial units for statistics) regions of Turkey. Density measures, trend extrapolation and regression analysis are the subject statistical methods used for projecting the land use. The findings show that using the chosen methodologies to project past changes leads in significant uncertainty. The results are significantly influenced by the variation in selected variables, and spatial organization of the study region. Therefore, validation analysis as a future research focus will be essential to select the most appropriate model that can be used to project the land use changes in Turkey. The results from the current analysis can be adopted by the government and local authorities for the land management and sustainable growth of urban land use in the Turkish regional context.

Spatial planning systems and institutions have a significant role in managing non-agricultural land growth in Europe and the assessment of how their implementation impacts on agricultural land consumption is of great significance for policy and institutional improvement. Reducing the area of agricultural land taken for urban development, or eliminating such conversion, is an international policy priority aiming to maintain the amount and quality of land resources currently available for food production and sustainable development. This study aimed to evaluate the impact of land use planning systems and institutional settings on urban conversion of agricultural land in the 265 NUTS2 level EU27 and UK regions. Taking these regions as the unit of our analysis, the research developed and used global and local econometrics models to estimate the effect based on socio-economic, institutional and land use data for the 2000–2018 period. There is limited research focusing on the impacts of institutional settings and planning types of the European countries on the conversion of agricultural land. Furthermore, existing research has not considered the spatial relationships with the determinants of agricultural land conversion and the response variable, therefore, our research aimed to contribute to the literature on the subject. The results showed that the types of spatial planning systems and institution variables significantly impact the conversion of agricultural land to urban uses. Socio-economic indicators and areas of agricultural and urban land have significant impact on agricultural land conversion for any type of spatial planning system. A further result was that decentralization and political fragmentation were positively associated with agricultural land conversion while quality of regional government and governance was negatively associated. A local regression model was assessed to explore the different spatial patterns of the relationships driving agricultural land conversion. The main empirical finding from this model was that there was spatial variation of driving factors of agricultural land conversion in Europe.

Development of composite indicators is a challenging task given that sustainability indices are strongly dependent on how the sub-indicators are weighted. This is because relative indicator weights may significantly differ based on the chosen weighting methods used in the analysis. There is hardly any study that has paid attention to this issue so far. Therefore, this paper aims to fill this gap in the literature by searching the robustness of selected weighting methods, i.e. entropy-weight (EW), principal component analysis (PCA), machine learning approaches (random forest-RF), regression analysis (RA) and benefit-of-the-doubt (BOD) when constructing a composite indicator. To research the current sustainability performance of European regions, the present study focuses on the Territorial Quality of Life Index—initially proposed by the ESPON Programme—that are aligned with the specific targets of the Sustainable Development Goals of the 2030 Agenda. The methods to construct composite indicators include stages of data preparation (including the estimation of missing values with random forest method), normalization, statistical transformation of raw data, reduction of indicators in order to ease public communication (using the PCA method) and data interpretation, weighting of the sub-indicators using EW, PCA, RF, RA and BOD methods and their linear weighted aggregation, and checking for robustness and sensitivity. The results suggest that there are significant differences in the rank and spatial distribution of composite indicators based on the use of different weighting methods considered in the analysis. The results from sensitivity analysis support the robustness of entropy-weight method among others. The methodology used in the current analysis can be adapted to other study areas and regions internationally. The findings showed that Eastern European countries and some Mediterranean countries have relatively lower index values compared to other European regions; therefore, policy and planning actions are needed covering these regions specifically.

The rapid increase of population and urban growth have caused huge challenges in ensuring and sustaining environmental and life quality in megacities. In this context, determining, measuring, and analysing the elements that influence city quality of life (QoL) have become important for sustainable urban growth and development. There is a growing interest in QoL assessments, yet reliable and transparent knowledge about the methodology is limited. In this regard, this study aims to contribute to the literature by achieving two distinctive objectives: (1) providing a methodology for investigating the robustness of different weighting approaches to produce a comprehensive and adaptable process for the construction of composite indicators; (2) measuring the urban quality of life at neighbourhood level by including geographical data. Data-dependent statistical methods i.e. Principal Component Analysis (PCA) and Entropy; and multi-criteria decision analysis (MCDA) techniques i.e. Analytic Hierarchy Process (AHP) and Best–Worst Method (BWM) were applied to provide objective and subjective weighting approaches, respectively when calculating the urban quality of life (UQoL) index. The findings showed that there is no considerable difference in the pattern and overall ratio of the weights calculated from different methods; nevertheless, the degree of the weights varies according to the applied method. The results from sensitivity analysis applied to the selected indicator weights covering each method used in the analysis represent the effect of alternative criteria weights on the overall results and the findings point to the weakness of data-dependent methods. The study’s methodology can be applied to similar situations at local, regional, and global scales.

‘UrbanOccupationsOETR_1844/45 Bursa Region Temettuat Agricultural Production Dataset’ is constructed first by manual data entry from the Ottoman temettuat registers (available at the Turkish Presidency State Archives of the Republic of Turkey – Department of Ottoman Archives, (ML. VRD. TMT. d.) collection) into our customized relational database and then by making a selection available in spreadsheet format.Our team located, curated, and extracted agricultural production data for a total of 81 geosampled villages from the Bursa region for 1844/45. We coded both agricultural production areas as well as product type using the Corine Land Cover nomenclature (CLC2018, https://land.copernicus.eu/user-corner/technical-library/corine-land-cover-nomenclature-guidelines/html) and made available only the category, ‘2.1.1 Non-irrigated arable land’ (https://land.copernicus.eu/user-corner/technical-library/corine-land-cover-nomenclature-guidelines/html/index-clc-211.html), as a proxy for grain production. 02122020-A5_Total count of agricultural entries CLC 2.1.1 coded, qualified (dönüm and convertible) and quantified (numerical value)provides data on the cultivated area in square meters of 4,657 units, belonging to 3,019 producers, of 2,892 households, in 81 villages. 02122020_T6_Total count of tithe tax entries CLC 2.1.1 coded, qualified (kile and convertible), and quantified (numerical value)provides data on production volumes in kgs of 4,516 units, belonging to 1,924 producers, of 1,873 households, in 70 villages.Please cite the below paper in your publications if you use the dataset: Ustaoglu, Eda, M. Erdem Kabadayı, and Petrus Johannes Gerrits, ‘The Estimation of Non-Irrigated Crop Area and Production Using the Regression Analysis Approach: A Case Study of Bursa Region (Turkey) in the Mid-Nineteenth Century’, PLOS ONE 16, no. 4 (30 April 2021): e0251091, https://doi.org/10.1371/journal.pone.0251091.

Land allocation priorities to urban green cover are usually neglected particularly in the countries of developing economies such as Turkey. Lack of urban green space can cause many social and physical problems among the residents. Therefore, urban planning and policy should incorporate suitable green land in the urban planning of cities to optimise the benefits obtained from urban green spaces. Land suitability analysis is a commonly used methodology which provides a framework for developing strategies in the planning of green land development. Two different approaches will be utilised for the assessment of suitability of land uses for urban agriculture, forest and natural vegetation in the Pendik district of Istanbul. Standardisation of values in criteria maps was done using the deterministic approach in the first case whereas fuzzy membership was utilised as an alternative in the second case. Analytical Hierarchical Process (AHP) was used for the weighting of sub-criteria and map layers were overlaid using the weighted linear combination using the GIS software. Geophysical factors, transport and services accessibility, land cover/use, blue and green amenities, soil properties, geology and erosion susceptibility are the main criteria selected for the assessment of urban green land suitability. The provision of suitable land for urban agriculture, forest and natural vegetation uses will provide a framework to the land use planning and decision support aimed at contributing to urban sustainable development.

This chapter focuses on the estimation of marginal and total external costs of road transportation in Turkey in terms of accidents, air pollution, climate change, noise, and traffic congestion. The study estimates marginal external costs for cars, light commercial vehicles (LCVs), heavy duty vehicles (HDVs), busses, and motorcycles, which comprise total vehicle fleet stock of the Turkish road transport sector. The researchers reviewed the literature of both local and international studies for the quantification and monetisation of the specified external costs of road transport. This will provide a base for the future studies on Turkish transport research and transport policy appraisal guidelines. The authors conclude that accidents are the most important externality of road use and that local air pollution and congestion appear to be more important than noise and climate change. This implies that priority should be given to road accidents, air pollution, and congestion alongside noise and global warming.

While the driving factors of urban growth and urban sprawl have repeatedly been studied, the implications for residential densities presumably differ in growing and shrinking regions. Thus far, those differences have received little attention. This paper examined the dynamics of urban growth and shrinkage across EU regions, using residential densities as an explanatory factor to analyse the underlying dynamics. To do so, detailed spatial data on various potentially relevant factors were used in regression methods to establish the relevance of those factors for residential expansion and densification in growing and shrinking EU regions between the years 2000 and 2010. We found that expansion and densification processes are affected by population size, prior residential density, land supply, accessibility, agricultural land rent, physical factors, public regulation, and regional characteristics. The results of this study can confirm that residential expansion is driven differently in declining regions than in regions with population growth. Models explaining residential density changes also yield different results in declining regions.

The historic reconstruction of residential land cover is of significance to uncover the human-environment relationship and its changing dynamics. Taking into account the historical census data and cadastral maps of seven villages, this study generated residential land cover maps for the Bursa Region in the 1850s using a model based on natural constraints, land zoning, socio-economic factors and residential suitability. Two different historical reconstructions were generated; one based on a high density residential model and another based on a low density model. The simulated landcover information was used as an ancillary data to redistribute aggregated census counts to fine scale raster cells. Two different statistical models were developed; one based on probability maps and the other applying regression models including Ordinary Least Squares (OLS) and Geographically Weighted Regression (GWR) models. The regression models were validated with historical census data of the 1840s. From regression models, socio-economic and physical characteristics, accessibility and natural amenities showed significant impacts on the distribution of population. Model validation analysis revealed that GWR is more accurate than OLS models. The generated residential land cover and gridded population datasets can provide a basis for the historical study of population and land use.

Agricultural land cover and its changing extent are directly related to human activities, which have an adverse impact on the environment and ecosystems. The historical knowledge of crop production and its cultivation area is a key element. Such data provide a base for monitoring and mapping spatio-temporal changes in agricultural land cover/use, which is of great significance to examine its impacts on environmental systems. Historical maps and related data obtained from historical archives can be effectively used for reconstruction purposes through using sample data from ground observations, government inventories, or other historical sources. This study considered historical population and cropland survey data obtained from Ottoman Archives and cropland suitability map, accessibility, and geophysical attributes as ancillary data to estimate non-irrigated crop production and its corresponding cultivation area in the 1840s Bursa Region, Turkey. We used the regression analysis approach to estimate agricultural land area and grain production for the unknown data points in the study region. We provide the spatial distribution of production and its cultivation area based on the estimates of regression models. The reconstruction can be used in line with future historical research aiming to model landscape, climate, and ecosystems to assess the impact of human activities on the environmental systems in preindustrial times in the Bursa Region context.

Land-use suitability analysis is of significance for the planning and management of cities. The main objective of this study was to develop a spatial model for land suitability assessment for peri-urban agricultural development with the use of geographic information systems (GIS) integrated with Multi Criteria Decision Analysis (MCDA) techniques. Physical attributes, land use, natural resources, accessibility, geology and soil properties were recognised as the key factors influencing land suitability for peri-urban agriculture in the study area. The study follows an integrated approach for the suitability assessment of agricultural development in Pendik District of Istanbul (Turkey) through integrating fuzzy logic model, Analytical Hierarchy Process (AHP) and the technique for order preference by similarity to ideal solution (TOPSIS) methods. Suitability classification of land was done in GIS by using the specified fuzzy membership functions. The weights of the main criteria and their sub-criteria were determined through the AHP method. Eight different zones were ranked according to agricultural development priority by the TOPSIS method. The area classified as suitable amounts to 27% of the total area with 0.07% classified as unsuitable. The highly suitable areas are mainly located in the north where there are villages specialised in agricultural production. The prioritisation of the suitable agricultural areas would assist in land use planning and urban management.

Urban green amenities provide many environmental and social benefits that are important for urban landscapes, natural ecosystems and their services. Amenity-led development is at the centre of local policies and regional growth strategies in that it reduces the impacts of rapid urbanisation on environment and society. Therefore, planners and policy makers often seek to optimise benefits of urban green spaces through developing programmes and strategies for the green amenity-oriented development in different urban areas and regions. Land suitability analysis is a widely utilised methodology that can help to establish strategies for the development of urban green land. This paper follows an integrated approach for the suitability analysis of urban green land development through integrating fuzzy set model and analytical hierarchical process (AHP) with the GIS-based multi-criteria decision making process. Pendik district in Istanbul (Turkey) was selected for the case study. Geo-physical factors, accessibility, blue and green amenities, residential centres, agricultural suitability, and land use/cover of the study area were recognised as the key factors affecting urban green land suitability. The results showed that most of the sites fall within marginal and low suitability class. About 25 % of the area was low suitable for green land development and 9 % was highly suitable. Land in the southern part of Pendik had higher suitability while northern region resulted in lower suitability. With a better understanding of potential sites that are suitable for urban green development, this study is fruitful for optimising land use planning and decision support.