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This paper aims to examine the dynamics of house prices in metropolitan cities in an emerging economy. The purpose of this study is to characterise the house price dynamics and the spatial heterogeneity in the dynamics.

The author explores spatial heterogeneity in house price dynamics, using data for 35 Indian cities with a million-plus population. The research methodology uses panel econometrics allowing for spatial heterogeneity, cross-sectional dependence and non-stationary data. The author tests for spatial differences and analyses the income elasticity of prices, the role of construction costs and lending to the real estate industry by commercial banks.

Long-term fundamentals drive the Indian housing markets, where wealth parameters are stronger than supply-side parameters such as construction costs or availability of financing for housing projects. The long-term elasticity of house prices to aggregate household deposits (wealth proxy) varies considerably across cities. However, the elasticity estimated at 0.39 is low. The highest coefficient is for Ludhiana (1.14), followed by Bhubaneswar (0.78). The short-term dynamics are robust and show spatial heterogeneity. Short-term momentum (lagged housing price changes) has a parameter value of 0.307. The momentum factor is the crucial dynamic in the short term. The second driver, the reversion rate to long-term equilibrium (estimated at −0.18), is higher than rates reported from developed markets.

This research applies to markets that require some home equity contributions from buyers of housing services.

Stakeholders can characterise stable housing markets based on long-term fundamental value and short-run house price dynamics. Because stable housing markets benefit all stakeholders, weak or non-existent mean reversion dynamics may prompt the intervention of policymakers. The role of urban planners, and local and regional governance, is essential to remove the bottlenecks from the demand side or supply side factors that can lead to runaway prices.

Existing literature is concerned about the risk of a housing bubble due to relaxed credit norms. To prevent housing market bubbles, some regulators require higher contributions from home buyers in the form of equity. The dynamics of house prices in markets with higher owner equity requirements vary from high-leverage markets. The influence of wealth effects is examined using novel data sets. This research, documents in an emerging market context, the observations cited in low-leverage developed markets such as Germany and Japan.

The susceptible-infectious-susceptible (SIS) infectious disease models without spatial heterogeneity have limited applications, and the numerical simulation without considering models’ global existence and uniqueness of classical solutions might converge to an impractical solution. This paper aims to develop a robust and reliable numerical approach to the SIS epidemic model with spatial heterogeneity, which characterizes the horizontal and vertical transmission of the disease.

This study used stability analysis methods from nonlinear dynamics to evaluate the stability of SIS epidemic models. Additionally, the authors applied numerical solution methods from diffusion equations and heat conduction equations in fluid mechanics to infectious disease transmission models with spatial heterogeneity, which can guarantee a robustly stable and highly reliable numerical process. The findings revealed that this interdisciplinary approach not only provides a more comprehensive understanding of the propagation patterns of infectious diseases across various spatial environments but also offers new application directions in the fields of fluid mechanics and heat flow. The results of this study are highly significant for developing effective control strategies against infectious diseases while offering new ideas and methods for related fields of research.

Through theoretical analysis and numerical simulation, the distribution of infected persons in heterogeneous environments is closely related to the location parameters. The finding is suitable for clinical use.

The theoretical analysis of the stability theorem and the threshold dynamics guarantee robust stability and fast convergence of the numerical solution. It opens up a new window for a robust and reliable numerical study.

The purpose of this study is to examine whether PV uptake is associated with key housing market determinants and linked to socio-economic profiles. An abundance of extant literature has examined the role of solar photovoltaic (PV) adoption and user costs, with an emerging corpus of literature investigating the role of the determinants of PV uptake, particularly in relation to the built environment and the spatial variation of PV dependency and dissimilarity. Despite this burgeoning literature, there remains limited insights from the UK perspective on housing market characteristics driving PV adoption and in relation spatial differences and heterogeneity that may exist.

Applying micro-based data at the Super Output Area-level geography, this study develops a series of ordinary least squares, spatial econometric models and a logistic regression analysis to examine built environment, housing tenure and deprivation attributes on PV adoption at the regional level in Northern Ireland, UK.

The findings emerging from the research reveal the presence of some spatial clustering and PV diffusion, in line with several existing studies. The findings demonstrate that an urban-rural dichotomy exists seemingly driven by social interaction and peer effects which has a profound impact on the likelihood of PV adoption. Further, the results exhibit tenure composition and “economic status” to be significant and important determinants of PV diffusion and uptake.

Housing market characteristics such as tenure composition across local market structures remain under-researched in relation to renewable energy uptake and adoption. This study examines the role of housing market attributes relative to socio-economic standing for adopting renewable energy.

This study aims to investigate the spatial distribution of housing prices and identify the affecting factors (independent variable) on the cost of residential units (dependent variable).

The method of the present study is descriptive-analytical and has an applied purpose. The used statistical population in this study is the residential units’ price in Tehran in 2021. For this purpose, the average per square meter of residential units in the city neighborhoods was entered in the geographical information system. Two techniques of ordinary least squares regression and geographically weighted regression have been used to analyze housing prices and modeling. Then, the results of the ordinary least squares regression and geographically weighted regression models were compared by using the housing price interpolation map predicted in each model and the accurate housing price interpolation map.

Based on the results, the ordinary least squares regression model has poorly modeled housing prices in the study area. The results of the geographically weighted regression model show that the variables (access rate to sports fields, distance from gas station and water station) have a direct and significant effect. Still, the variable (distance from fault) has a non-significant impact on increasing housing prices at a city level. In addition, to identify the affecting variables of housing prices, the results confirm the desirability of the geographically weighted regression technique in terms of accuracy compared to the ordinary least squares regression technique in explaining housing prices. The results of this study indicate that the housing prices in Tehran are affected by the access level to urban services and facilities.

Identifying factors affecting housing prices helps create sustainable housing in Tehran. Building sustainable housing represents spending less energy during the construction process together with the utilization phase, which ultimately provides housing at an acceptable price for all income deciles. In housing construction, the more you consider the sustainable housing principles, the more sustainable housing you provide and you take a step toward sustainable development. Therefore, sustainable housing is an important planning factor for local authorities and developers. As a result, it is necessary to institutionalize an integrated vision based on the concepts of sustainable development in the field of housing in the Tehran metropolis.

This article aims to analyse the spatial and temporal evolution of multidimensional poverty in Brazil's North Region and its relationship with territorial, economic and population dynamics.

A multidimensional poverty index (MPI) was calculated using the Alkire-Foster method and a spatial econometric model was estimated. The data come from population censuses conducted by the Brazilian Institute of Geography and Statistics (IBGE) for the years 1991, 2000 and 2010.

The results show a decrease in multidimensional poverty over the period analysed. However, they show this reduction occurred in a heterogeneous way in time and space, with emphasis on microregions in which, despite a reduction in the rate, the percentage of the population considered multidimensionally poor remained high during the 30 years of the study.

The quality of available data.

It is possible to point out that public policies focused on improving infrastructure in medium-sized locations tend to have two positive effects: first, making production and consumption more accessible and sustainable for local communities, given that currently the cost of transport and logistics are factors that hinder socioeconomic development; second, to reduce the pressure of demand that this population of small and medium-sized locations exerts on public services currently offered only in larger locations. Although the logic of concentrating most public services in the largest cities makes sense in terms of economic efficiency, the particularities of the northern region and the recognition of its environmental importance point to the need for incentives for more spatially distributed economic activity.

From the paper results, it is possible to think about more local public policies which are able to improve people's lifes without to damage the environment.

This is the first study on multidimensional poverty that covers the entire North region of Brazil (Amazon region) and that contemplates both the temporal and spatial dynamics of poverty.

The peer review history for this article is available at: https://publons.com/publon/10.1108/IJSE-11-2021-0699.

The choice of crops to produce at a location depends to a large degree on the climate. As the climate changes and food demand evolves, farmers may need to produce a different mix of crops. This study assesses how much cropland may be subject to such upheavals at the global scale, and then focuses on China as a case study to examine how spatial heterogeneity informs different contexts for adaptation within a country.

A global agricultural economic model is linked to a cropland allocation algorithm to generate maps of cropland distribution under historical and future conditions. The mix of crops at each location is examined to determine whether it is likely to experience a major shift.

Two-thirds of rainfed cropland and half of irrigated cropland are likely to experience substantial upheaval of some kind.

This analysis helps establish a global context for the local changes that producers might face under future climate and socioeconomic changes. The scale of the challenge means that the agricultural sector needs to prepare for these widespread and diverse upheavals.

By discussing the spatial spillover effect and regional heterogeneity of digital economy and green agricultural development level, this paper aims to provide countermeasures and suggestions for the better development of green agriculture in the contemporary era when digital economy is universally developed and at the same time provide development suggestions suitable for green agriculture's development characteristics and initial conditions for different regions.

This paper discusses the theoretical foundation of the digital economy and green agriculture development and utilizes panel data from 30 provinces in China from 2011 to 2018. By employing the Super-Efficiency Slack-based Measure and Malmquist-Luenberger (SBM-ML) model based on unexpected output to measure the total factor productivity of green agriculture and employing the spatial panel Durbin model to empirically test the spatiotemporal effects of the digital economy on green agriculture development from both temporal and spatial dimensions. Finally, the model is tested for robustness as well as heterogeneity.

The research findings are as follows: First, from the perspective of time effect, digital economy has a continuous driving effect on the development of green agriculture and with the passage of time, this effect becomes more and more prominent; second, from the perspective of spatial effect, digital economy has a significant positive impact on the development of local green agriculture, while digital economy has a significant negative impact on the development of surrounding green agriculture. Finally, the impact of digital economy on the development of green agriculture shows significant differences in different dimensions and regions.

As an important driver of economic growth, the digital economy has injected new impetus into agricultural and rural development. Along with the intensifying environmental pollution problems, how to influence the green development of agriculture through the digital economy is a proposition worthy of attention nowadays. This paper analyzes the relationship between the digital economy and agricultural green development in multiple dimensions by exploring the temporal and spatial spillover effects of the digital economy on agricultural green development, as well as the heterogeneity in different dimensions and in different regions and derives policy insights accordingly in order to improve relevant policies.

This paper evaluates the level of the digital economy in Chinese cities based on digital industrialization and industrial digitalization. The research focuses on the effects of spatial mechanism of the urban digital economy on the quality of firms’ exported products.

The authors use the principal component analysis method to evaluate the level of China’s urban digital economy, and spatial metrology to measure the spatial effects of the digital economy on product quality.

The findings suggest that the urban digital economy can expand the quality of firms’ exports. The digital economy has spatial dependence, spatial spillover and spatial heterogeneity on product quality. At the same time, the spatial effect has a significant nonlinear effect and threshold effect. Further decomposition shows that industrial digitalization is the core factor of enterprises’ export products quality, and the micro-mechanism of this impact is mainly manifested in optimization of resource allocation.

The innovation of this paper is reflected explicitly in exploring the quality upgrading of export products from the background of the digital economy, providing a reference for the improvement of China’s export trade competitiveness and the cultivation of a trade power. The authors studied two different mechanisms (specialization division of labor and optimization of resource allocation) to explain the spatial imbalance of export product quality to provide empirical support for enterprises and government departments to formulate quality upgrading policies accurately.

Infrastructure financialization plays a critical role in infrastructure development and urban growth around the world. However, on the one hand, the existing research on the infrastructure financialization focuses on qualitative and lacks quantitative country-specific studies. On the other hand, the spatial heterogeneity and influencing factors of infrastructure financialization are ignored. This study takes China as a typical case to identify and analyze the spatial characteristics, development process and impact factors of infrastructure financialization.

To assess the development and characteristics of infrastructure financialization in China, this study constructs an evaluation index of infrastructure financialization based on the infrastructure financialization ratio (IFR). This study then analyzes the evolution process and spatial pattern of China's infrastructure financialization through the spatial analysis method. Furthermore, this study identifies and quantitatively analyzes the influencing factors of infrastructure financialization based on the spatial Dubin model. Finally, this study offers a policy suggestion as a governance response.

The results demonstrate that infrastructure financialization effectively promotes the development of infrastructure in China. Second, there are significant spatial differences in China’s infrastructure financialization. Third, many factors affect infrastructure financialization, with government participation having the greatest impact. In addition, over-financialization of infrastructure has the potential to lead to government debt risks, which is a critical challenge the Chinese Government must address. Finally, this study suggests that infrastructure financialization requires more detailed, tailored,and place-specific policy interventions by the government.

This study not only contributes to enriching the knowledge body of global financialization theory but also helps optimize infrastructure investment and financing policies in China and provides peer reference for other developing countries.

Micro-regeneration can effectively enhance a neighborhood’s commercial vitality and serve as a viable approach to boost economic benefits. However, the small scale of micro-regeneration efforts and the fragmented nature of information currently limit the availability of strong empirical evidence demonstrating its impact on neighborhood commercial vitality. The aim of the study was to examine the link between micro-regeneration and neighborhood commercial vitality, focusing on the average, time-lag, spatial spillover, and spatial heterogeneity effects.

Using the panel data set of 1,755 neighborhoods in Chongqing from 2016 to 2021 as the research sample, the difference-in-differences (DID) method was employed in this study to explore the impact micro-regeneration has on neighborhood commercial vitality.

The results illustrate that: (1) micro-regeneration can promote neighborhood commercial vitality in terms of the number and types of local consumption amenities by 27.76 and 5.89%, respectively, with no time-lag effect; (2) the positive spillovers can exist within the range of 5,000 meters–5,500 meters of regenerated neighborhoods; and (3) the effect of micro-regeneration on neighborhood commercial vitality can be greater in peripheral areas than in core areas of the city.

The findings fill the knowledge gap on the relationship between micro-regeneration and neighborhood commercial vitality. Additionally, the results on the time-lag effect, spatial spillover effects, and spatial heterogeneity provide practical implications that can support the government and private sector in developing temporal and spatial arrangements for micro-regeneration projects.